From 9f023ff5655af9ab6a4ecaf58c464627530c09e7 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Mon, 4 Dec 2023 12:38:45 +0100
Subject: [PATCH 01/23] Add EI_clustered_network to PyNEST examples

---
 .../Network_Schematic.png                     | Bin 0 -> 250599 bytes
 .../examples/EI_clustered_network/README.rst  |  67 ++
 .../default_parameters.py                     | 123 ++++
 .../EI_clustered_network/general_helper.py    |  97 +++
 .../examples/EI_clustered_network/helper.py   | 118 ++++
 .../examples/EI_clustered_network/network.py  | 652 ++++++++++++++++++
 .../EI_clustered_network/run_simulation.py    |  81 +++
 7 files changed, 1138 insertions(+)
 create mode 100644 pynest/examples/EI_clustered_network/Network_Schematic.png
 create mode 100644 pynest/examples/EI_clustered_network/README.rst
 create mode 100644 pynest/examples/EI_clustered_network/default_parameters.py
 create mode 100644 pynest/examples/EI_clustered_network/general_helper.py
 create mode 100644 pynest/examples/EI_clustered_network/helper.py
 create mode 100644 pynest/examples/EI_clustered_network/network.py
 create mode 100644 pynest/examples/EI_clustered_network/run_simulation.py

diff --git a/pynest/examples/EI_clustered_network/Network_Schematic.png b/pynest/examples/EI_clustered_network/Network_Schematic.png
new file mode 100644
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diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
new file mode 100644
index 0000000000..1b14d49c32
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -0,0 +1,67 @@
+EI-clustered circuit model
+===========================
+
+This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1]_.
+
+.. figure:: Network_Schematic.png
+   :scale: 50 %
+   :alt: EI-clustered circuit model
+
+   Schematic of the EI-clustered circuit model. The network consists of Q clusters with one excitatory and one inhibitory population each.
+
+Citing this code
+----------------
+
+If you use this code, we ask you to cite the paper by Rostami et al. [1]_ and the NEST release on Zenodo.
+
+File structure
+--------------
+* `run_simulation.py`: an example script to try out the EI-clustered circuit model
+* `network.py`: the main Network class with functions to build and simulate the network
+* `helper.py`: helper functions for calculation of synaptic weights and currents
+* `general_helper.py`: helper functions to merge dictionaries and plot function for raster plots
+* `default_parameters.py`: default parameters for the network and simulation
+
+Running the simulation
+----------------------
+
+.. code-block:: bash
+
+   python3 run_simulation.py
+
+A raster plot of the network activity is saved as ``Rasterplot.png``.
+
+The code can be parallelized by using multiple threads during the NEST simulation.
+This can be done by setting the parameter ``n_jobs`` in the ``run_simulation.py`` script.
+
+Contributions to this PyNEST model implementation
+--------------------------------------------------------------
+2023: inital bersion of code and documentation by Felix J. Schmitt, Vahid Rostami and Martin Nawrot.
+
+Acknowledgments
+---------------
+
+Funding for the study by Rostami et al. [1]_: This work was supported by the German Research Foundation (DFG),
+in parts through the Collaborative Research Center ’Motor Control in Health and Disease’
+(DFG-SFB 1451, Project-ID 431549029) and under the Institutional Strategy of the University of Cologne within the
+German Excellence Initiative (DFG-ZUK 81/1) and in parts through the DFG graduate school
+’Neural Circuit Analysis’ (DFG-RTG 1960, ID 365082554) and through the European Union’s Horizon 2020 Framework
+Programme for Research and Innovation under grant agreement number 945539 (Human Brain Project SGA3).
+
+Other implementations of the EI-clustered model
+-----------------------------------------------
+
+A `GeNN version <https://github.com/nawrotlab/SNN_GeNN_Nest>`__  by Felix J. Schmitt, Vahid Rostami and Martin Nawrot [2]_.
+
+References
+----------
+
+.. [1]  Rostami V, Rost T, Riehle A, van Albada SJ and Nawrot MP. 2020.
+        Excitatory and inhibitory motor cortical clusters account for balance, variability, and task performance.
+        bioRxiv 2020.02.27.968339. DOI: `10.1101/2020.02.27.968339 <https://doi.org/10.1101/2020.02.27.968339>`__.
+
+
+.. [2]  Schmitt FJ, Rostami V and Nawrot MP. 2023.
+        Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN
+        and NEST. Front. Neuroinform. 17:941696. DOI: `10.3389/fninf.2023.941696 <https://doi.org/10.3389/fninf.2023.941696>`__.
+
diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
new file mode 100644
index 0000000000..507457920f
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+#
+# helper.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: default parameters for EI-clustered network
+-------------------------------------------
+
+default parameters for EI-clustered network simulation
+"""
+import numpy as np
+
+############################################
+# general parameters
+############################################
+eps = np.finfo(float).eps
+n_jobs = 1
+
+dt=0.1
+simtime=1000.
+warmup=0.
+record_voltage = False
+record_from = 'all'
+recording_interval = dt
+return_weights = False
+
+############################################
+# neuron parameters
+############################################
+neuron_type = 'iaf_psc_exp' # 'gif_psc_exp'
+E_L = 0.
+C_m = 1.
+tau_E = 20.
+tau_I = 10.
+t_ref = 5.
+V_th_E = 20.
+V_th_I = 20.
+V_r = 0.
+I_xE = 1.
+I_xI = 2.
+delta_I_xE = 0.
+delta_I_xI = 0.
+I_th_E = 1.25
+I_th_I = 0.78
+V_m = 'rand'
+
+# synapse parameters
+tau_syn_ex = 3.
+tau_syn_in = 2.
+delay = 0.1  # synaptic delay
+
+#Distribution of synaptic weights
+#available distributions= https://nest-simulator.readthedocs.io/en/stable/guides/connection_management.html#dist-params
+
+DistParams={'distribution':'normal', 'sigma': 0.0, 'fraction': False}
+
+syn_params={"U": 0.2, "u": 0.0, "tau_rec": 120.0,
+                  "tau_fac": 0.0}
+
+
+############################################
+# network parameters
+############################################
+
+# number of units
+N_E = 1200
+N_I = 300
+
+# cluster number
+Q = 6
+# cluster weight ratios
+jplus = np.ones((2,2))
+
+# connection probabilities
+ps = np.array([[0.2,0.5],[0.5,0.5]])
+
+# connections strengths
+# weights are js/sqrt(N)
+# nan means they are calculated
+js = np.ones((2,2))*np.nan
+# factors for inhibitory weights
+ge = 1.2
+gi = 1.
+gie = 1.
+
+# factor multiplied with weights
+s = 1.
+fixed_indegree = False
+
+############################################
+# stimulation parameters
+############################################
+stim_clusters = None # clusters to be stimulated
+stim_amp = 0.  # amplitude of the stimulation current in pA
+stim_starts = []      # list of stimulation start times
+stim_ends = []          # list of stimulation end times
+
+
+
+
+
+
+
+
+
+
+
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
new file mode 100644
index 0000000000..e9fd539629
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+#
+# helper.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: general helper functions
+-------------------------------------------
+
+Helper functions for merging dictionaries and some plotting functions.
+"""
+import matplotlib.pyplot as plt
+from types import ModuleType
+import copy
+import numpy as np
+
+def merge_params(params, default_values):
+    """
+    Updates default Values defined in a module with params-dictionary.
+    :param params: dictionary of parameters
+    :param default_values: module with default values
+    :return: updated dictionary
+    """
+    return nested_update(module_to_dict(default_values), params)
+
+
+
+def module_to_dict(module):
+    """
+    Creates dict of explicit variables in module which are not imported modules.
+    Generates for each variable name an entry in the dictionary.
+    :param module: module
+    :return: dict of explicit variables
+    """
+    module_dict = {}
+    if module:
+        module_dict = {key: value for key, value in module.__dict__.items() if not (key.startswith('__') or key.startswith('_') or isinstance(value, ModuleType))}
+    return module_dict
+
+
+def nested_update(d, d2):
+    """
+    Updates values in d with values of d2. Adds new keys if they are not present.
+    :param d: dictionary to be updated
+    :param d2: dictionary with update values
+    :return: updated dictionary
+    """
+    d_local=copy.deepcopy(d)
+    for key in d2:
+        if isinstance(d2[key], dict) and key in d_local:
+            d_local[key] = nested_update(d_local[key], d2[key])
+        else:
+            d_local[key] = d2[key]
+    return d_local
+
+def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
+    """
+    Plots raster plot of spiketimes.
+    :param spiketimes: spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
+    :param tlim: time limits of plot (list): [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
+    :param colorgroups: list of tuples (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons, if None: all black
+    :param ax: axis object, if None: new figure
+    :param markersize: size of markers
+    :return: axis object
+    """
+    if ax is None:
+        fig, ax = plt.subplots()
+    if tlim is None:
+        tlim = [min(spiketimes[0]), max(spiketimes[0])]
+    if colorgroups is None:
+        colorgroups = [('k', 0, max(spiketimes[1]))]
+    for color, start, stop in colorgroups:
+        ax.plot(spiketimes[0][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+                spiketimes[1][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+                color=color, marker='.', linestyle='None', markersize=markersize)
+    ax.set_xlim(tlim)
+    ax.set_ylim([0, max(spiketimes[1])])
+    ax.set_xlabel('Time [ms]')
+    ax.set_ylabel('Neuron ID')
+    return ax
+
+
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
new file mode 100644
index 0000000000..74fb21bf33
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+#
+# helper.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: Helper Functions
+-------------------------------------------
+
+Helper functions to calculate synaptic weights and first passage times for exponential synapses to construct
+random balanced networks.
+"""
+
+import numpy as np
+small = 1e-10
+
+
+def max_psp_exp(tau_m, tau_syn, c_m=1., e_l=0.):
+    """
+    Calculates the maximum psp amplitude for exponential synapses and unit J.
+    :param tau_m: Membrane time constant [ms]
+    :param tau_syn: Synapse time constant  [ms]
+    :param c_m: Membrane capacity [pF]
+    :param e_l: Resting potential [mV]
+    :return: maximum psp amplitude
+    """
+    tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)
+    B = tau_m * tau_syn / c_m / (tau_syn - tau_m)
+    return (e_l - B) * np.exp(-tmax / tau_m) + B * np.exp(-tmax / tau_syn)
+
+
+def calc_js(params):
+    """
+    calculates the synaptic weights for exponential synapses before clustering
+    :param params: dictionary of network parameters
+    :return: synaptic weights 2x2 matrix
+    """
+    N_E = params.get('N_E')  # excitatory units
+    N_I = params.get('N_I')  # inhibitory units
+    N = N_E + N_I  # total units
+    ps = params.get('ps')  # connection probs
+    ge = params.get('ge')
+    gi = params.get('gi')
+    gie = params.get('gie')
+    V_th_E = params.get('V_th_E')  # threshold voltage
+    V_th_I = params.get('V_th_I')
+    tau_E = params.get('tau_E')
+    tau_I = params.get('tau_I')
+    E_L = params.get('E_L')
+    neuron_type = params.get('neuron_type')
+    if ('iaf_psc_exp' in neuron_type) or ('gif_psc_exp' in neuron_type):
+        tau_syn_ex = params.get('tau_syn_ex')
+        tau_syn_in = params.get('tau_syn_in')
+        amp_EE = max_psp_exp(tau_E, tau_syn_ex)
+        amp_EI = max_psp_exp(tau_E, tau_syn_in)
+        amp_IE = max_psp_exp(tau_I, tau_syn_ex)
+        amp_II = max_psp_exp(tau_I, tau_syn_in)
+    else:
+        amp_EE = 1.
+        amp_EI = 1.
+        amp_IE = 1.
+        amp_II = 1.
+
+    js = np.zeros((2, 2))
+    K_EE = N_E * ps[0, 0]
+    js[0, 0] = (V_th_E - E_L) * (K_EE ** -0.5) * N ** 0.5 / amp_EE
+    js[0, 1] = -ge * js[0, 0] * ps[0, 0] * N_E * amp_EE / (ps[0, 1] * N_I * amp_EI)
+    K_IE = N_E * ps[1, 0]
+    js[1, 0] = gie * (V_th_I - E_L) * (K_IE ** -0.5) * N ** 0.5 / amp_IE
+    js[1, 1] = -gi * js[1, 0] * ps[1, 0] * N_E * amp_IE / (ps[1, 1] * N_I * amp_II)
+    return js
+
+
+def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
+    """
+    calculate first pasage time between Vstart and Vtarget.
+    :param tau_m: Membrane time constant [ms]
+    :param e_l: Resting potential [mV]
+    :param i_e: external current [pA]
+    :param c_m: Membrane capacity [pF]
+    :param vtarget: target voltage [mV]
+    :param vstart: start voltage [mV]
+    :return: first passage time [ms]
+    """
+    inner = (vtarget - e_l - tau_m * i_e / c_m) / (vstart - e_l - tau_m * i_e / c_m + small)
+    if inner < 0:
+        return np.nan
+    else:
+        return -tau_m * np.log(inner)
+
+def v_fpt(tau_m, e_l, i_e, c_m, ttarget, vtarget, t_ref):
+    """
+    Calculate the initial voltage required to obtain a certain first passage time.
+    :param tau_m: Membrane time constant [ms]
+    :param e_l: Resting potential [mV]
+    :param i_e: external current [pA]
+    :param c_m: Membrane capacity [pF]
+    :param ttarget: target first passage time [ms]
+    :param vtarget: target voltage [mV]
+    :param t_ref: refractory period [ms]
+    :return: initial voltage [mV]
+    """
+    return (vtarget - e_l - tau_m * i_e / c_m) * np.exp(ttarget / tau_m) + e_l + tau_m * i_e / c_m
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
new file mode 100644
index 0000000000..ed972d2a19
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -0,0 +1,652 @@
+# -*- coding: utf-8 -*-
+#
+# network.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: Network Class
+----------------------------------------
+
+Main file of the EI-clustered network defining the ``ClusteredNetwork`` class with functions to
+build and simulate the network.
+
+"""
+
+import nest
+import numpy as np
+import pickle
+import helper
+import general_helper
+
+
+class ClusteredNetwork(object):
+    """ Provides functions to create neuron populations, stimulation devices and recording devices for an EI-clustered network
+    and setups the simulation in NEST (v3.x).
+
+    Creates an object with functions to create neuron populations,
+    stimulation devices and recording devices for an EI-clustered network.
+    Initializes the object. Creates the attributes Populations, RecordingDevices and
+    Currentsources to be filled during network construction.
+    Attribute params contains all parameters used to construct network.
+
+    Parameters:
+        defaultValues (module): A Module which contains the default configuration
+        parameters (dict):      Dictionary with parameters which should be modified from their default values
+    """
+
+    def __init__(self, defaultValues, parameters):
+        self.params = general_helper.merge_params(parameters, defaultValues)
+        self.Populations = []
+        self.RecordingDevices = []
+        self.Currentsources = []
+        self.ModelBuildPipeline = [self.setup_nest, self.create_populations, self.create_stimulation,
+                                   self.create_recording_devices, self.connect]
+
+    @classmethod
+    def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
+        """ Creates a ClusteredNetwork object with parameters Rj and Rep
+
+        Rj and Rep are used to calculate the parameters jplus (weight clustering) or pplus (probability clustering)
+
+        Parameters:
+            Rj (float):           Ratio excitatory to inhibitory clustering strength
+            Rep (float):          Excitatory clustering strength
+            defaultValues (module): A Module which contains the default configuration
+            parameters (dict):      Dictionary with parameters which should be modified from their default values
+
+        Returns:
+            object: ClusteredNetwork object with parameters
+        """
+        params = general_helper.merge_params(parameters, defaultValues)
+        if params['clustering'] == 'weight':
+            jep = Rep
+            jip = 1. + (jep - 1) * Rj
+            params['jplus'] = np.array([[jep, jip], [jip, jip]])
+        elif params['clustering'] == 'probabilities':
+            pep = Rep
+            pip = 1. + (pep - 1) * Rj
+            params['pplus'] = np.array([[pep, pip], [pip, pip]])
+        else:
+            raise ValueError("Clustering type not recognized")
+        return cls(defaultValues, params)
+
+
+    def clean_network(self):
+        """ Creates clean network
+
+        Creates empty attributes of a network.
+        """
+        self.Populations = []
+        self.RecordingDevices = []
+        self.Currentsources = []
+
+    def setup_nest(self):
+        """ Initializes the NEST kernel.
+
+        Reset the NEST kernel and pass parameters to it.
+        Updates randseed of parameters to the actual used one if none is supplied.
+        """
+        nest.ResetKernel()
+        nest.set_verbosity('M_WARNING')
+        nest.local_num_threads = self.params.get('n_jobs', 1)
+        nest.resolution = self.params.get('dt')
+        self.params['randseed'] = self.params.get('randseed', np.random.randint(1000000))
+        nest.rng_seed = self.params.get('randseed')
+        #nest.print_time = True
+
+    def create_populations(self):
+        """ Create all neuron populations.
+
+        Q excitatory and inhibitory neuron populations with the parameters of the network are created.
+
+        """
+        # make sure number of clusters and units are compatible
+        assert self.params['N_E'] % self.params['Q'] == 0, 'N_E needs to be evenly divisible by Q'
+        assert self.params['N_I'] % self.params['Q'] == 0, 'N_I needs to be evenly divisible by Q'
+
+        N = self.params['N_E'] + self.params['N_I']  # total units
+        try:
+            DistParams = self.params['DistParams']
+        except AttributeError:
+            DistParams = {'distribution': 'normal', 'sigma': 0.0, 'fraction': False}
+
+        if self.params['I_th_E'] is None:
+            I_xE = self.params['I_xE']
+        else:
+            I_xE = self.params['I_th_E'] * (self.params['V_th_E'] - self.params['E_L']) / self.params['tau_E'] * \
+                   self.params['C_m']
+
+        if self.params['I_th_I'] is None:
+            I_xI = self.params['I_xI']
+        else:
+            I_xI = self.params['I_th_I'] * (self.params['V_th_I'] - self.params['E_L']) / self.params['tau_I'] * \
+                   self.params['C_m']
+
+        E_neuron_params = {'E_L': self.params['E_L'], 'C_m': self.params['C_m'], 'tau_m': self.params['tau_E'],
+                           't_ref': self.params['t_ref'], 'V_th': self.params['V_th_E'], 'V_reset': self.params['V_r'],
+                           'I_e': I_xE}
+        I_neuron_params = {'E_L': self.params['E_L'], 'C_m': self.params['C_m'], 'tau_m': self.params['tau_I'],
+                           't_ref': self.params['t_ref'], 'V_th': self.params['V_th_I'], 'V_reset': self.params['V_r'],
+                           'I_e': I_xI}
+        if 'iaf_psc_exp' in self.params['neuron_type']:
+            E_neuron_params['tau_syn_ex'] = self.params['tau_syn_ex']
+            E_neuron_params['tau_syn_in'] = self.params['tau_syn_in']
+            I_neuron_params['tau_syn_in'] = self.params['tau_syn_in']
+            I_neuron_params['tau_syn_ex'] = self.params['tau_syn_ex']
+
+            # iaf_psc_exp allows stochasticity, if not used - ignore
+            try:
+                if self.params['delta_'] is not None:
+                    E_neuron_params['delta'] = self.params['delta_']
+                    I_neuron_params['delta'] = self.params['delta_']
+                if self.params['rho'] is not None:
+                    E_neuron_params['rho'] = self.params['rho']
+                    I_neuron_params['rho'] = self.params['rho']
+            except KeyError:
+                pass
+        else:
+            assert 'iaf_psc_exp' in self.params['neuron_type'], "iaf_psc_exp neuron model is the only implemented model"
+
+            # create the neuron populations
+        E_pops = []
+        I_pops = []
+        for q in range(self.params['Q']):
+            E_pops.append(nest.Create(self.params['neuron_type'], int(self.params['N_E'] / self.params['Q'])))
+            nest.SetStatus(E_pops[-1], E_neuron_params)
+        for q in range(self.params['Q']):
+            I_pops.append(nest.Create(self.params['neuron_type'], int(self.params['N_I'] / self.params['Q'])))
+            nest.SetStatus(I_pops[-1], I_neuron_params)
+
+        if self.params['delta_I_xE'] > 0:
+            for E_pop in E_pops:
+                I_xEs = nest.GetStatus(E_pop, 'I_e')
+                nest.SetStatus(E_pop, [
+                    {'I_e': (1 - 0.5 * self.params['delta_I_xE'] + np.random.rand() * self.params['delta_I_xE']) * ixe}
+                    for ixe in I_xEs])
+
+        if self.params['delta_I_xI'] > 0:
+            for I_pop in I_pops:
+                I_xIs = nest.GetStatus(I_pop, 'I_e')
+                nest.SetStatus(I_pop, [
+                    {'I_e': (1 - 0.5 * self.params['delta_I_xI'] + np.random.rand() * self.params['delta_I_xI']) * ixi}
+                    for ixi in I_xIs])
+        if self.params['V_m'] == 'rand':
+            T_0_E = self.params['t_ref'] + helper.fpt(self.params['tau_E'], self.params['E_L'], I_xE,
+                                                      self.params['C_m'], self.params['V_th_E'],
+                                                      self.params['V_r'])
+            if np.isnan(T_0_E):
+                T_0_E = 10.
+            for E_pop in E_pops:
+                nest.SetStatus(E_pop, [{'V_m': helper.v_fpt(self.params['tau_E'], self.params['E_L'], I_xE,
+                                                            self.params['C_m'], T_0_E * np.random.rand(),
+                                                            self.params['V_th_E'], self.params['t_ref'])} for i
+                                       in range(len(E_pop))])
+
+            T_0_I = self.params['t_ref'] + helper.fpt(self.params['tau_I'], self.params['E_L'], I_xI,
+                                                      self.params['C_m'], self.params['V_th_I'],
+                                                      self.params['V_r'])
+            if np.isnan(T_0_I):
+                T_0_I = 10.
+            for I_pop in I_pops:
+                nest.SetStatus(I_pop, [{'V_m': helper.v_fpt(self.params['tau_I'], self.params['E_L'], I_xI,
+                                                            self.params['C_m'], T_0_I * np.random.rand(),
+                                                            self.params['V_th_E'], self.params['t_ref'])} for i
+                                       in range(len(I_pop))])
+        else:
+            nest.SetStatus(nest.NodeCollection([x for x in range(1, N + 1)]),
+                           [{'V_m': self.params['V_m']} for i in range(N)])
+        self.Populations = [E_pops, I_pops]
+
+
+    def connect(self):
+        """
+        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        """
+        # if CLustering is set to "Weight" or is not set at all or is not in dictionary, use the connect_weight function
+        try:
+            if self.params['clustering'] == 'weight':
+                self.connect_weight()
+            elif self.params['clustering'] == 'probabilities':
+                self.connect_probabilities()
+            else:
+                raise ValueError('Clustering method %s not implemented' % self.params['clustering'])
+        except KeyError:
+            self.connect_weight()
+
+    def connect_probabilities(self):
+        """ Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+            by increasing the probabilies of the connections within the clusters and decreasing the probabilites of the
+            connections between the clusters. The weights are calculated so that the total input to a neuron
+            is balanced.
+        """
+        #  self.Populations[0] -> Excitatory population
+        #  self.Populations[1] -> Inhibitory population
+        # connectivity parameters
+        js = self.params['js']  # connection weights
+        N = self.params['N_E'] + self.params['N_I']  # total units
+        if np.isnan(js).any():
+            js = helper.calc_js(self.params)
+        js *= self.params['s']
+
+        if self.params['Q'] > 1:
+            pminus = (self.params['Q'] - self.params['pplus']) / float(self.params['Q'] - 1)
+        else:
+            self.params['pplus'] = np.ones((2, 2))
+            pminus = np.ones((2, 2))
+
+        p_plus= self.params['pplus']*self.params['ps']
+        p_minus = pminus*self.params['ps']
+
+        iterations = np.ones((2, 2), dtype=int)
+        # test if any of the probabilities is larger than 1
+        if np.any(p_plus > 1):
+            print('The probability of connection is larger than 1')
+            print('p_plus: ', p_plus)
+            print('p_minus: ', p_minus)
+            for i in range(2):
+                for j in range(2):
+                    if p_plus[i, j] > 1:
+                        iterations[i, j] = int(np.ceil(p_plus[i, j]))
+                        p_plus[i, j] /= iterations[i, j]
+            print('p_plus: ', p_plus)
+            print(iterations)
+
+        # define the synapses and connect the populations
+        # EE
+        j_ee = js[0, 0] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "EE",
+                       {"weight": j_ee, "delay": self.params['delay']})
+
+
+        if self.params['fixed_indegree']:
+            K_EE_plus = int(p_plus[0, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_EE+: ', K_EE_plus)
+            K_EE_minus = int(p_minus[0, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_EE-: ', K_EE_minus)
+            conn_params_EE_plus = {'rule': 'fixed_indegree', 'indegree': K_EE_plus, 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_EE_minus = {'rule': 'fixed_indegree', 'indegree': K_EE_minus, 'allow_autapses': False,
+                                     'allow_multapses': True}
+
+        else:
+            conn_params_EE_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[0, 0], 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_EE_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[0, 0], 'allow_autapses': False,
+                                   'allow_multapses': True}
+        for i, pre in enumerate(self.Populations[0]):
+            for j, post in enumerate(self.Populations[0]):
+                if i == j:
+                    # same cluster
+                    for n in range(iterations[0, 0]):
+                        nest.Connect(pre, post, conn_params_EE_plus, 'EE')
+                else:
+                    nest.Connect(pre, post, conn_params_EE_minus, 'EE')
+
+        # EI
+        j_ei = js[0, 1] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "EI",
+                       {"weight": j_ei, "delay": self.params['delay']})
+
+        if self.params['fixed_indegree']:
+            K_EI_plus = int(p_plus[0, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_EI+: ', K_EI_plus)
+            K_EI_minus = int(p_minus[0, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_EI-: ', K_EI_minus)
+            conn_params_EI_plus = {'rule': 'fixed_indegree', 'indegree': K_EI_plus, 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_EI_minus = {'rule': 'fixed_indegree', 'indegree': K_EI_minus, 'allow_autapses': False,
+                                     'allow_multapses': True}
+
+        else:
+            conn_params_EI_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[0, 1], 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_EI_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[0, 1], 'allow_autapses': False,
+                                   'allow_multapses': True}
+        for i, pre in enumerate(self.Populations[1]):
+            for j, post in enumerate(self.Populations[0]):
+                if i == j:
+                    # same cluster
+                    for n in range(iterations[0, 1]):
+                        nest.Connect(pre, post, conn_params_EI_plus, 'EI')
+                else:
+                    nest.Connect(pre, post, conn_params_EI_minus, 'EI')
+
+        # IE
+        j_ie = js[1, 0] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "IE",
+                          {"weight": j_ie, "delay": self.params['delay']})
+
+        if self.params['fixed_indegree']:
+            K_IE_plus = int(p_plus[1, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_IE+: ', K_IE_plus)
+            K_IE_minus = int(p_minus[1, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_IE-: ', K_IE_minus)
+            conn_params_IE_plus = {'rule': 'fixed_indegree', 'indegree': K_IE_plus, 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_IE_minus = {'rule': 'fixed_indegree', 'indegree': K_IE_minus, 'allow_autapses': False,
+                                     'allow_multapses': True}
+
+        else:
+            conn_params_IE_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[1, 0], 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_IE_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[1, 0], 'allow_autapses': False,
+                                   'allow_multapses': True}
+        for i, pre in enumerate(self.Populations[0]):
+            for j, post in enumerate(self.Populations[1]):
+                if i == j:
+                    # same cluster
+                    for n in range(iterations[1, 0]):
+                        nest.Connect(pre, post, conn_params_IE_plus, 'IE')
+                else:
+                    nest.Connect(pre, post, conn_params_IE_minus, 'IE')
+
+        # II
+        j_ii = js[1, 1] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "II",
+                            {"weight": j_ii, "delay": self.params['delay']})
+
+        if self.params['fixed_indegree']:
+            K_II_plus = int(p_plus[1, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_II+: ', K_II_plus)
+            K_II_minus = int(p_minus[1, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_II-: ', K_II_minus)
+            conn_params_II_plus = {'rule': 'fixed_indegree', 'indegree': K_II_plus, 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_II_minus = {'rule': 'fixed_indegree', 'indegree': K_II_minus, 'allow_autapses': False,
+                                     'allow_multapses': True}
+
+        else:
+            conn_params_II_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[1, 1], 'allow_autapses': False,
+                              'allow_multapses': True}
+            conn_params_II_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[1, 1], 'allow_autapses': False,
+                                   'allow_multapses': True}
+        for i, pre in enumerate(self.Populations[1]):
+            for j, post in enumerate(self.Populations[1]):
+                if i == j:
+                    # same cluster
+                    for n in range(iterations[1, 1]):
+                        nest.Connect(pre, post, conn_params_II_plus, 'II')
+                else:
+                    nest.Connect(pre, post, conn_params_II_minus, 'II')
+
+
+    def connect_weight(self):
+        """ Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+            by increasing the weights of the connections within the clusters and decreasing the weights of the
+            connections between the clusters. The weights are calculated so that the total input to a neuron
+            is balanced.
+        """
+        #  self.Populations[0] -> Excitatory population
+        #  self.Populations[1] -> Inhibitory population
+        # connectivity parameters
+        js = self.params['js']  # connection weights
+        N = self.params['N_E'] + self.params['N_I']  # total units
+
+        # if js are not given compute them so that sqrt(K) spikes equal v_thr-E_L and rows are balanced
+        if np.isnan(js).any():
+            js = helper.calc_js(self.params)
+        js *= self.params['s']
+
+        # jminus is calculated so that row sums remain constant
+        if self.params['Q'] > 1:
+            jminus = (self.params['Q'] - self.params['jplus']) / float(self.params['Q'] - 1)
+        else:
+            self.params['jplus'] = np.ones((2, 2))
+            jminus = np.ones((2, 2))
+
+        # define the synapses and connect the populations
+        # EE
+        j_ee = js[0, 0] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "EE_plus",
+                       {"weight": self.params['jplus'][0, 0] * j_ee, "delay": self.params['delay']})
+        nest.CopyModel("static_synapse", "EE_minus", {"weight": jminus[0, 0] * j_ee, "delay": self.params['delay']})
+        if self.params['fixed_indegree']:
+            K_EE = int(self.params['ps'][0, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_EE: ', K_EE)
+            conn_params_EE = {'rule': 'fixed_indegree', 'indegree': K_EE, 'allow_autapses': False,
+                              'allow_multapses': False}
+
+        else:
+            conn_params_EE = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][0, 0], 'allow_autapses': False,
+                              'allow_multapses': False}
+        for i, pre in enumerate(self.Populations[0]):
+            for j, post in enumerate(self.Populations[0]):
+                if i == j:
+                    # same cluster
+                    nest.Connect(pre, post, conn_params_EE, 'EE_plus')
+                else:
+                    nest.Connect(pre, post, conn_params_EE, 'EE_minus')
+
+        # EI
+        j_ei = js[0, 1] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "EI_plus",
+                       {"weight": j_ei * self.params['jplus'][0, 1], "delay": self.params['delay']})
+        nest.CopyModel("static_synapse", "EI_minus", {"weight": j_ei * jminus[0, 1], "delay": self.params['delay']})
+        if self.params['fixed_indegree']:
+            K_EI = int(self.params['ps'][0, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_EI: ', K_EI)
+            conn_params_EI = {'rule': 'fixed_indegree', 'indegree': K_EI, 'allow_autapses': False,
+                              'allow_multapses': False}
+        else:
+            conn_params_EI = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][0, 1], 'allow_autapses': False,
+                              'allow_multapses': False}
+        for i, pre in enumerate(self.Populations[1]):
+            for j, post in enumerate(self.Populations[0]):
+                if i == j:
+                    # same cluster
+                    nest.Connect(pre, post, conn_params_EI, 'EI_plus')
+                else:
+                    nest.Connect(pre, post, conn_params_EI, 'EI_minus')
+        # IE
+        j_ie = js[1, 0] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "IE_plus",
+                       {"weight": j_ie * self.params['jplus'][1, 0], "delay": self.params['delay']})
+        nest.CopyModel("static_synapse", "IE_minus", {"weight": j_ie * jminus[1, 0], "delay": self.params['delay']})
+
+        if self.params['fixed_indegree']:
+            K_IE = int(self.params['ps'][1, 0] * self.params['N_E'] / self.params['Q'])
+            print('K_IE: ', K_IE)
+            conn_params_IE = {'rule': 'fixed_indegree', 'indegree': K_IE, 'allow_autapses': False,
+                              'allow_multapses': False}
+        else:
+            conn_params_IE = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][1, 0], 'allow_autapses': False,
+                              'allow_multapses': False}
+        for i, pre in enumerate(self.Populations[0]):
+            for j, post in enumerate(self.Populations[1]):
+                if i == j:
+                    # same cluster
+                    nest.Connect(pre, post, conn_params_IE, 'IE_plus')
+                else:
+                    nest.Connect(pre, post, conn_params_IE, 'IE_minus')
+
+        # II
+        j_ii = js[1, 1] / np.sqrt(N)
+        nest.CopyModel("static_synapse", "II_plus",
+                       {"weight": j_ii * self.params['jplus'][1, 1], "delay": self.params['delay']})
+        nest.CopyModel("static_synapse", "II_minus", {"weight": j_ii * jminus[1, 1], "delay": self.params['delay']})
+        if self.params['fixed_indegree']:
+            K_II = int(self.params['ps'][1, 1] * self.params['N_I'] / self.params['Q'])
+            print('K_II: ', K_II)
+            conn_params_II = {'rule': 'fixed_indegree', 'indegree': K_II, 'allow_autapses': False,
+                              'allow_multapses': False}
+        else:
+            conn_params_II = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][1, 1], 'allow_autapses': False,
+                              'allow_multapses': False}
+        for i, pre in enumerate(self.Populations[1]):
+            for j, post in enumerate(self.Populations[1]):
+                if i == j:
+                    # same cluster
+                    nest.Connect(pre, post, conn_params_II, 'II_plus')
+                else:
+                    nest.Connect(pre, post, conn_params_II, 'II_minus')
+        #print('Js: ', js / np.sqrt(N))
+
+    def create_stimulation(self):
+        """
+        Creates a current source and connects it to the specified cluster/s.
+        """
+        if self.params['stim_clusters'] is not None:
+            stim_amp = self.params['stim_amp']  # amplitude of the stimulation current in pA
+            stim_starts = self.params['stim_starts']  # list of stimulation start times
+            stim_ends = self.params['stim_ends']  # list of stimulation end times
+            amplitude_values = []
+            amplitude_times = []
+            for start, end in zip(stim_starts, stim_ends):
+                amplitude_times.append(start + self.params['warmup'])
+                amplitude_values.append(stim_amp)
+                amplitude_times.append(end + self.params['warmup'])
+                amplitude_values.append(0.)
+            self.Currentsources = [nest.Create('step_current_generator')]
+            for stim_cluster in self.params['stim_clusters']:
+                nest.Connect(self.Currentsources[0], self.Populations[0][stim_cluster])
+            nest.SetStatus(self.Currentsources[0],
+                           {'amplitude_times': amplitude_times, 'amplitude_values': amplitude_values})
+
+    def create_recording_devices(self):
+        """
+        Creates a spike recorder connected to all neuron populations created by create_populations
+        """
+        self.RecordingDevices = [nest.Create("spike_recorder")]
+        self.RecordingDevices[0].record_to = "memory"
+
+        all_units = self.Populations[0][0]
+        for E_pop in self.Populations[0][1:]:
+            all_units += E_pop
+        for I_pop in self.Populations[1]:
+            all_units += I_pop
+        nest.Connect(all_units, self.RecordingDevices[0], "all_to_all")  # Spikerecorder
+
+    def set_model_build_pipeline(self, Pipeline):
+        """
+        Sets the ModelBuildPipeline.
+        Parameters:
+            Pipeline (list of functions):   ordered list of functions executed to build the network model
+        """
+        self.ModelBuildPipeline = Pipeline
+
+    def setup_network(self):
+        """
+        Initializes NEST and creates the network in NEST, ready to be simulated.
+        Functions saved in ModelBuildPipeline are executed.
+        nest.Prepare is executed in this function.
+        """
+        for func in self.ModelBuildPipeline:
+            func()
+        nest.Prepare()
+
+
+    def simulate(self):
+        """
+        Simulates network for a period of warmup+simtime
+        """
+        if self.params['warmup'] + self.params['simtime'] <= 0.1:
+            pass
+        else:
+            nest.Run(self.params['warmup'] + self.params['simtime'])
+
+    def get_recordings(self):
+        """
+        Extracts spikes form the Spikerecorder connected to all populations created in create_populations.
+        Cuts the warmup period away and sets time relative to end of warmup.
+        Ids 1:N_E correspond to excitatory neurons, N_E+1:N_E+N_I correspond to inhibitory neurons.
+
+        Returns:
+            spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
+        """
+        events = nest.GetStatus(self.RecordingDevices[0], 'events')[0]
+        # convert them to the format accepted by spiketools
+        spiketimes = np.append(events['times'][None, :], events['senders'][None, :], axis=0)
+        spiketimes[1] -= 1
+        # remove the pre warmup spikes
+        spiketimes = spiketimes[:, spiketimes[0] >= self.params['warmup']]
+        spiketimes[0] -= self.params['warmup']
+        return spiketimes
+
+    def get_parameter(self):
+        """
+        Returns all parameters used to create the network.
+        Return:
+            parameters (dict): Dictionary with all parameters for the simulation / network creation.
+        """
+        return self.params
+
+    def create_and_simulate(self):
+        """
+        Creates the EI-clustered network and simulates it with the parameters supplied in the object creation.
+
+        Returns:
+            spiketimes (np.array):  Row 0: spiketimes, Row 1: neuron ID.
+                                    Ids 1:N_E correspond to excitatory neurons,
+                                    N_E+1:N_E+N_I correspond to inhibitory neurons.
+        """
+        self.setup_network()
+        self.simulate()
+        return self.get_recordings()
+
+    def get_firing_rates(self, spiketimes=None):
+        """
+        Calculates the firing rates of all excitatory neurons and the firing rates of all inhibitory neurons
+        created by self.create_populations. If spiketimes are not supplied, they get extracted.
+        Parameters:
+            spiketimes: (optional, np.array 2xT)   spiketimes of simulation
+        Returns:
+            (e_rate, i_rate) average firing rate of excitatory/inhibitory neurons (spikes/s)
+        """
+        if spiketimes is None:
+            spiketimes = self.get_recordings()
+        e_count = spiketimes[:, spiketimes[1] < self.params['N_E']].shape[1]
+        i_count = spiketimes[:, spiketimes[1] >= self.params['N_E']].shape[1]
+        e_rate = e_count / float(self.params['N_E']) / float(self.params['simtime']) * 1000.
+        i_rate = i_count / float(self.params['N_I']) / float(self.params['simtime']) * 1000.
+        return e_rate, i_rate
+
+    def set_I_x(self, I_XE, I_XI):
+        """
+        Adds DC currents for the excitatory and inhibitory neurons. Allows also the usage of a baseline defined by the
+        model setup.
+        """
+        for E_pop in self.Populations[0]:
+            I_e_loc = E_pop.get('I_e')
+            E_pop.set({'I_e': I_e_loc + I_XE})
+        for I_pop in self.Populations[1]:
+            I_e_loc = I_pop.get('I_e')
+            I_pop.set({'I_e': I_e_loc + I_XI})
+
+    def get_simulation(self, PathSpikes=None):
+        """
+        Creates the network, simulates it and extracts the firing rates. If PathSpikes is supplied the spikes get saved
+        to a pickle file. If a timeout is supplied, a timeout handler is created which stops the execution.
+        Parameters:
+            PathSpikes: (optional) Path of file for spiketimes
+            timeout: (optional) Time of timeout in seconds
+        Returns:
+            Dictionary with firing rates, timing information (dict) and parameters (dict)
+        """
+
+        self.setup_network()
+        self.simulate()
+        spiketimes = self.get_recordings()
+        e_rate, i_rate = self.get_firing_rates(spiketimes)
+
+        if PathSpikes is not None:
+            with open(PathSpikes, 'wb') as outfile:
+                pickle.dump(spiketimes, outfile)
+
+        nest.Cleanup()
+        return {'e_rate': e_rate, 'i_rate': i_rate, 'params': self.get_parameter(),
+                'spiketimes': spiketimes}
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
new file mode 100644
index 0000000000..9e3b17ce6a
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+#
+# network.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: Run Simulation
+----------------------------------------
+
+This is an example script for running the EI-clustered model with two stimulations
+and generating a raster plot.
+"""
+
+
+import numpy as np
+import default_parameters as default
+import network
+import general_helper
+import matplotlib.pyplot as plt
+
+if __name__ == '__main__':
+    # set the parameters for the simulation
+    params = {'n_jobs': 12,  #number of threads used for parallelization in NEST
+              'N_E': 4000,  #number of excitatory neurons
+              'N_I': 1000,   #number of inhibitory neurons
+              'E_L': 0.,    #leak potential
+              'V_th_E': 20.,#threshold potential for excitatory neurons
+              'V_th_I': 20.,#threshold potential for inhibitory neurons
+              'V_r': 0.,    #reset potential
+              'C_m': 1.,    #membrane capacity
+              'tau_E': 20., #membrane time constant for excitatory neurons
+              'tau_I': 10., #membrane time constant for inhibitory neurons
+              't_ref': 5.,  #refractory period
+              'ps': np.array([[0.2, 0.5], [0.5, 0.5]]), #connection probabilities
+              'ge': 1.2,    #excitatory synaptic weight factor
+              'I_th_E': 1.25, #Background current for excitatory neurons in multiples of I_rheobase
+              'I_th_I': 0.78, #Background current for inhibitory neurons in multiples of I_rheobase
+              'dt': 0.1,    #simulation time step
+              'neuron_type': 'iaf_psc_exp', #neuron model used in NEST iaf and gif are available
+              'simtime': 10000, #simulation time in ms
+              'warmup': 1000,  #warmup time in ms (time before recording starts)
+              'Q': 20, #number of clusters
+              'clustering': 'weight', #type of clustering used 'weight' or
+              'stim_clusters': [2,3,4], #list of clusters which are stimulated
+              'stim_starts': [2000,6000], #list of stimulation start times
+              'stim_ends': [3500,7500], #list of stimulation end times
+              'stim_amp': 0.15, #amplitude of the stimulation current in pA
+              #'probabilities'
+              }
+
+    Rj= 0.82  # ration excitatory to inhibitory clustering
+    Rep = 6.  # excitatory clustering strength
+
+    # Creates object which creates the EI clustered network in NEST
+    EI_Network = network.ClusteredNetwork.Clustered_Network_from_Rj_Rep(Rj, Rep, default, params)
+
+    # Runs the simulation and returns the spiketimes
+    # get simulation initializes the network in NEST and runs the simulation
+    # it returns a dict with the average rates, the spiketimes and the used parameters
+    Result = EI_Network.get_simulation()
+    ax = general_helper.raster_plot(Result['spiketimes'], tlim=(0, params['simtime']),colorgroups=[
+        ('k', 0, params['N_E']), ("darkred", params['N_E'], params['N_E'] + params['N_I'])])
+    plt.savefig('Rasterplot.png')
+    print('Firing rate of excitatory neurons: ' + str(Result['e_rate']) + ' Spikes/s')
+    print('Firing rate of inhibitory neurons: ' + str(Result['i_rate']) + ' Spikes/s')
+

From 625115d33e5a33712811bd731f9c2e69313d8233 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Thu, 14 Dec 2023 14:53:05 +0100
Subject: [PATCH 02/23] Fix issues with copyright headers

---
 pynest/examples/EI_clustered_network/default_parameters.py | 2 +-
 pynest/examples/EI_clustered_network/general_helper.py     | 2 +-
 pynest/examples/EI_clustered_network/run_simulation.py     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index 507457920f..49e866d1bf 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# helper.py
+# default_parameters.py
 #
 # This file is part of NEST.
 #
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index e9fd539629..aa419d66ed 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# helper.py
+# general_helper.py
 #
 # This file is part of NEST.
 #
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 9e3b17ce6a..fe7df07eb7 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# network.py
+# run_simulation.py
 #
 # This file is part of NEST.
 #

From dd61cbcb9b21e5270f898283c86bc020c4450621 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Thu, 14 Dec 2023 14:59:21 +0100
Subject: [PATCH 03/23] Fix issues with too long lines and trailing newlines

---
 .../default_parameters.py                     | 42 +++++---------
 .../EI_clustered_network/general_helper.py    | 12 ++--
 .../examples/EI_clustered_network/network.py  |  4 +-
 .../EI_clustered_network/run_simulation.py    | 58 +++++++++----------
 4 files changed, 51 insertions(+), 65 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index 49e866d1bf..428f0b3767 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -32,9 +32,9 @@
 eps = np.finfo(float).eps
 n_jobs = 1
 
-dt=0.1
-simtime=1000.
-warmup=0.
+dt = 0.1
+simtime = 1000.
+warmup = 0.
 record_voltage = False
 record_from = 'all'
 recording_interval = dt
@@ -43,7 +43,7 @@
 ############################################
 # neuron parameters
 ############################################
-neuron_type = 'iaf_psc_exp' # 'gif_psc_exp'
+neuron_type = 'iaf_psc_exp'  # 'gif_psc_exp'
 E_L = 0.
 C_m = 1.
 tau_E = 20.
@@ -65,14 +65,13 @@
 tau_syn_in = 2.
 delay = 0.1  # synaptic delay
 
-#Distribution of synaptic weights
-#available distributions= https://nest-simulator.readthedocs.io/en/stable/guides/connection_management.html#dist-params
+# Distribution of synaptic weights
+# available distributions= https://nest-simulator.readthedocs.io/en/stable/guides/connection_management.html#dist-params
 
-DistParams={'distribution':'normal', 'sigma': 0.0, 'fraction': False}
-
-syn_params={"U": 0.2, "u": 0.0, "tau_rec": 120.0,
-                  "tau_fac": 0.0}
+DistParams = {'distribution': 'normal', 'sigma': 0.0, 'fraction': False}
 
+syn_params = {"U": 0.2, "u": 0.0, "tau_rec": 120.0,
+              "tau_fac": 0.0}
 
 ############################################
 # network parameters
@@ -85,15 +84,15 @@
 # cluster number
 Q = 6
 # cluster weight ratios
-jplus = np.ones((2,2))
+jplus = np.ones((2, 2))
 
 # connection probabilities
-ps = np.array([[0.2,0.5],[0.5,0.5]])
+ps = np.array([[0.2, 0.5], [0.5, 0.5]])
 
 # connections strengths
 # weights are js/sqrt(N)
 # nan means they are calculated
-js = np.ones((2,2))*np.nan
+js = np.ones((2, 2)) * np.nan
 # factors for inhibitory weights
 ge = 1.2
 gi = 1.
@@ -106,18 +105,7 @@
 ############################################
 # stimulation parameters
 ############################################
-stim_clusters = None # clusters to be stimulated
+stim_clusters = None  # clusters to be stimulated
 stim_amp = 0.  # amplitude of the stimulation current in pA
-stim_starts = []      # list of stimulation start times
-stim_ends = []          # list of stimulation end times
-
-
-
-
-
-
-
-
-
-
-
+stim_starts = []  # list of stimulation start times
+stim_ends = []  # list of stimulation end times
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index aa419d66ed..6abd28dbc6 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -29,6 +29,7 @@
 import copy
 import numpy as np
 
+
 def merge_params(params, default_values):
     """
     Updates default Values defined in a module with params-dictionary.
@@ -39,7 +40,6 @@ def merge_params(params, default_values):
     return nested_update(module_to_dict(default_values), params)
 
 
-
 def module_to_dict(module):
     """
     Creates dict of explicit variables in module which are not imported modules.
@@ -49,7 +49,8 @@ def module_to_dict(module):
     """
     module_dict = {}
     if module:
-        module_dict = {key: value for key, value in module.__dict__.items() if not (key.startswith('__') or key.startswith('_') or isinstance(value, ModuleType))}
+        module_dict = {key: value for key, value in module.__dict__.items() if
+                       not (key.startswith('__') or key.startswith('_') or isinstance(value, ModuleType))}
     return module_dict
 
 
@@ -60,7 +61,7 @@ def nested_update(d, d2):
     :param d2: dictionary with update values
     :return: updated dictionary
     """
-    d_local=copy.deepcopy(d)
+    d_local = copy.deepcopy(d)
     for key in d2:
         if isinstance(d2[key], dict) and key in d_local:
             d_local[key] = nested_update(d_local[key], d2[key])
@@ -68,12 +69,13 @@ def nested_update(d, d2):
             d_local[key] = d2[key]
     return d_local
 
+
 def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
     """
     Plots raster plot of spiketimes.
     :param spiketimes: spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
     :param tlim: time limits of plot (list): [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
-    :param colorgroups: list of tuples (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons, if None: all black
+    :param colorgroups: list of tuples (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons, if None: black
     :param ax: axis object, if None: new figure
     :param markersize: size of markers
     :return: axis object
@@ -93,5 +95,3 @@ def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5
     ax.set_xlabel('Time [ms]')
     ax.set_ylabel('Neuron ID')
     return ax
-
-
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index ed972d2a19..0112a3d118 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -35,8 +35,8 @@
 
 
 class ClusteredNetwork(object):
-    """ Provides functions to create neuron populations, stimulation devices and recording devices for an EI-clustered network
-    and setups the simulation in NEST (v3.x).
+    """ Provides functions to create neuron populations, stimulation devices and recording devices for an
+    EI-clustered network and setups the simulation in NEST (v3.x).
 
     Creates an object with functions to create neuron populations,
     stimulation devices and recording devices for an EI-clustered network.
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index fe7df07eb7..4d502cf599 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -26,7 +26,6 @@
 and generating a raster plot.
 """
 
-
 import numpy as np
 import default_parameters as default
 import network
@@ -35,35 +34,35 @@
 
 if __name__ == '__main__':
     # set the parameters for the simulation
-    params = {'n_jobs': 12,  #number of threads used for parallelization in NEST
-              'N_E': 4000,  #number of excitatory neurons
-              'N_I': 1000,   #number of inhibitory neurons
-              'E_L': 0.,    #leak potential
-              'V_th_E': 20.,#threshold potential for excitatory neurons
-              'V_th_I': 20.,#threshold potential for inhibitory neurons
-              'V_r': 0.,    #reset potential
-              'C_m': 1.,    #membrane capacity
-              'tau_E': 20., #membrane time constant for excitatory neurons
-              'tau_I': 10., #membrane time constant for inhibitory neurons
-              't_ref': 5.,  #refractory period
-              'ps': np.array([[0.2, 0.5], [0.5, 0.5]]), #connection probabilities
-              'ge': 1.2,    #excitatory synaptic weight factor
-              'I_th_E': 1.25, #Background current for excitatory neurons in multiples of I_rheobase
-              'I_th_I': 0.78, #Background current for inhibitory neurons in multiples of I_rheobase
-              'dt': 0.1,    #simulation time step
-              'neuron_type': 'iaf_psc_exp', #neuron model used in NEST iaf and gif are available
-              'simtime': 10000, #simulation time in ms
-              'warmup': 1000,  #warmup time in ms (time before recording starts)
-              'Q': 20, #number of clusters
-              'clustering': 'weight', #type of clustering used 'weight' or
-              'stim_clusters': [2,3,4], #list of clusters which are stimulated
-              'stim_starts': [2000,6000], #list of stimulation start times
-              'stim_ends': [3500,7500], #list of stimulation end times
-              'stim_amp': 0.15, #amplitude of the stimulation current in pA
-              #'probabilities'
+    params = {'n_jobs': 12,  # number of threads used for parallelization in NEST
+              'N_E': 4000,  # number of excitatory neurons
+              'N_I': 1000,  # number of inhibitory neurons
+              'E_L': 0.,  # leak potential
+              'V_th_E': 20.,  # threshold potential for excitatory neurons
+              'V_th_I': 20.,  # threshold potential for inhibitory neurons
+              'V_r': 0.,  # reset potential
+              'C_m': 1.,  # membrane capacity
+              'tau_E': 20.,  # membrane time constant for excitatory neurons
+              'tau_I': 10.,  # membrane time constant for inhibitory neurons
+              't_ref': 5.,  # refractory period
+              'ps': np.array([[0.2, 0.5], [0.5, 0.5]]),  # connection probabilities
+              'ge': 1.2,  # excitatory synaptic weight factor
+              'I_th_E': 1.25,  # Background current for excitatory neurons in multiples of I_rheobase
+              'I_th_I': 0.78,  # Background current for inhibitory neurons in multiples of I_rheobase
+              'dt': 0.1,  # simulation time step
+              'neuron_type': 'iaf_psc_exp',  # neuron model used in NEST iaf and gif are available
+              'simtime': 10000,  # simulation time in ms
+              'warmup': 1000,  # warmup time in ms (time before recording starts)
+              'Q': 20,  # number of clusters
+              'clustering': 'weight',  # type of clustering used 'weight' or
+              'stim_clusters': [2, 3, 4],  # list of clusters which are stimulated
+              'stim_starts': [2000, 6000],  # list of stimulation start times
+              'stim_ends': [3500, 7500],  # list of stimulation end times
+              'stim_amp': 0.15,  # amplitude of the stimulation current in pA
+              # 'probabilities'
               }
 
-    Rj= 0.82  # ration excitatory to inhibitory clustering
+    Rj = 0.82  # ration excitatory to inhibitory clustering
     Rep = 6.  # excitatory clustering strength
 
     # Creates object which creates the EI clustered network in NEST
@@ -73,9 +72,8 @@
     # get simulation initializes the network in NEST and runs the simulation
     # it returns a dict with the average rates, the spiketimes and the used parameters
     Result = EI_Network.get_simulation()
-    ax = general_helper.raster_plot(Result['spiketimes'], tlim=(0, params['simtime']),colorgroups=[
+    ax = general_helper.raster_plot(Result['spiketimes'], tlim=(0, params['simtime']), colorgroups=[
         ('k', 0, params['N_E']), ("darkred", params['N_E'], params['N_E'] + params['N_I'])])
     plt.savefig('Rasterplot.png')
     print('Firing rate of excitatory neurons: ' + str(Result['e_rate']) + ' Spikes/s')
     print('Firing rate of inhibitory neurons: ' + str(Result['i_rate']) + ' Spikes/s')
-

From 9a7f79da4f4f06fa3767b3f9f2e6422001ee1a4a Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Thu, 14 Dec 2023 15:07:50 +0100
Subject: [PATCH 04/23] Used isort on files to fix imports

---
 pynest/examples/EI_clustered_network/general_helper.py | 5 +++--
 pynest/examples/EI_clustered_network/helper.py         | 1 +
 pynest/examples/EI_clustered_network/network.py        | 7 ++++---
 pynest/examples/EI_clustered_network/run_simulation.py | 4 ++--
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index 6abd28dbc6..6683cf730d 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -24,9 +24,10 @@
 
 Helper functions for merging dictionaries and some plotting functions.
 """
-import matplotlib.pyplot as plt
-from types import ModuleType
 import copy
+from types import ModuleType
+
+import matplotlib.pyplot as plt
 import numpy as np
 
 
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index 74fb21bf33..2d91f47937 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -27,6 +27,7 @@
 """
 
 import numpy as np
+
 small = 1e-10
 
 
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 0112a3d118..4a8bf1a361 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -27,11 +27,12 @@
 
 """
 
-import nest
-import numpy as np
 import pickle
-import helper
+
 import general_helper
+import helper
+import nest
+import numpy as np
 
 
 class ClusteredNetwork(object):
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 4d502cf599..39f260b12c 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -26,11 +26,11 @@
 and generating a raster plot.
 """
 
-import numpy as np
 import default_parameters as default
-import network
 import general_helper
 import matplotlib.pyplot as plt
+import network
+import numpy as np
 
 if __name__ == '__main__':
     # set the parameters for the simulation

From c0473fbfc0f9c25c799f3f451bc0db5ca6500574 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Thu, 14 Dec 2023 15:19:50 +0100
Subject: [PATCH 05/23] Use black to reformat code

---
 .../default_parameters.py                     |  51 +-
 .../EI_clustered_network/general_helper.py    |  28 +-
 .../examples/EI_clustered_network/helper.py   |  55 +-
 .../examples/EI_clustered_network/network.py  | 808 ++++++++++++------
 .../EI_clustered_network/run_simulation.py    |  84 +-
 5 files changed, 658 insertions(+), 368 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index 428f0b3767..9a9affa487 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -33,45 +33,44 @@
 n_jobs = 1
 
 dt = 0.1
-simtime = 1000.
-warmup = 0.
+simtime = 1000.0
+warmup = 0.0
 record_voltage = False
-record_from = 'all'
+record_from = "all"
 recording_interval = dt
 return_weights = False
 
 ############################################
 # neuron parameters
 ############################################
-neuron_type = 'iaf_psc_exp'  # 'gif_psc_exp'
-E_L = 0.
-C_m = 1.
-tau_E = 20.
-tau_I = 10.
-t_ref = 5.
-V_th_E = 20.
-V_th_I = 20.
-V_r = 0.
-I_xE = 1.
-I_xI = 2.
-delta_I_xE = 0.
-delta_I_xI = 0.
+neuron_type = "iaf_psc_exp"  # 'gif_psc_exp'
+E_L = 0.0
+C_m = 1.0
+tau_E = 20.0
+tau_I = 10.0
+t_ref = 5.0
+V_th_E = 20.0
+V_th_I = 20.0
+V_r = 0.0
+I_xE = 1.0
+I_xI = 2.0
+delta_I_xE = 0.0
+delta_I_xI = 0.0
 I_th_E = 1.25
 I_th_I = 0.78
-V_m = 'rand'
+V_m = "rand"
 
 # synapse parameters
-tau_syn_ex = 3.
-tau_syn_in = 2.
+tau_syn_ex = 3.0
+tau_syn_in = 2.0
 delay = 0.1  # synaptic delay
 
 # Distribution of synaptic weights
 # available distributions= https://nest-simulator.readthedocs.io/en/stable/guides/connection_management.html#dist-params
 
-DistParams = {'distribution': 'normal', 'sigma': 0.0, 'fraction': False}
+DistParams = {"distribution": "normal", "sigma": 0.0, "fraction": False}
 
-syn_params = {"U": 0.2, "u": 0.0, "tau_rec": 120.0,
-              "tau_fac": 0.0}
+syn_params = {"U": 0.2, "u": 0.0, "tau_rec": 120.0, "tau_fac": 0.0}
 
 ############################################
 # network parameters
@@ -95,17 +94,17 @@
 js = np.ones((2, 2)) * np.nan
 # factors for inhibitory weights
 ge = 1.2
-gi = 1.
-gie = 1.
+gi = 1.0
+gie = 1.0
 
 # factor multiplied with weights
-s = 1.
+s = 1.0
 fixed_indegree = False
 
 ############################################
 # stimulation parameters
 ############################################
 stim_clusters = None  # clusters to be stimulated
-stim_amp = 0.  # amplitude of the stimulation current in pA
+stim_amp = 0.0  # amplitude of the stimulation current in pA
 stim_starts = []  # list of stimulation start times
 stim_ends = []  # list of stimulation end times
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index 6683cf730d..4215c04756 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -50,8 +50,15 @@ def module_to_dict(module):
     """
     module_dict = {}
     if module:
-        module_dict = {key: value for key, value in module.__dict__.items() if
-                       not (key.startswith('__') or key.startswith('_') or isinstance(value, ModuleType))}
+        module_dict = {
+            key: value
+            for key, value in module.__dict__.items()
+            if not (
+                key.startswith("__")
+                or key.startswith("_")
+                or isinstance(value, ModuleType)
+            )
+        }
     return module_dict
 
 
@@ -86,13 +93,18 @@ def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5
     if tlim is None:
         tlim = [min(spiketimes[0]), max(spiketimes[0])]
     if colorgroups is None:
-        colorgroups = [('k', 0, max(spiketimes[1]))]
+        colorgroups = [("k", 0, max(spiketimes[1]))]
     for color, start, stop in colorgroups:
-        ax.plot(spiketimes[0][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
-                spiketimes[1][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
-                color=color, marker='.', linestyle='None', markersize=markersize)
+        ax.plot(
+            spiketimes[0][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+            spiketimes[1][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+            color=color,
+            marker=".",
+            linestyle="None",
+            markersize=markersize,
+        )
     ax.set_xlim(tlim)
     ax.set_ylim([0, max(spiketimes[1])])
-    ax.set_xlabel('Time [ms]')
-    ax.set_ylabel('Neuron ID')
+    ax.set_xlabel("Time [ms]")
+    ax.set_ylabel("Neuron ID")
     return ax
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index 2d91f47937..2d7cbcc46b 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -31,7 +31,7 @@
 small = 1e-10
 
 
-def max_psp_exp(tau_m, tau_syn, c_m=1., e_l=0.):
+def max_psp_exp(tau_m, tau_syn, c_m=1.0, e_l=0.0):
     """
     Calculates the maximum psp amplitude for exponential synapses and unit J.
     :param tau_m: Membrane time constant [ms]
@@ -51,38 +51,38 @@ def calc_js(params):
     :param params: dictionary of network parameters
     :return: synaptic weights 2x2 matrix
     """
-    N_E = params.get('N_E')  # excitatory units
-    N_I = params.get('N_I')  # inhibitory units
+    N_E = params.get("N_E")  # excitatory units
+    N_I = params.get("N_I")  # inhibitory units
     N = N_E + N_I  # total units
-    ps = params.get('ps')  # connection probs
-    ge = params.get('ge')
-    gi = params.get('gi')
-    gie = params.get('gie')
-    V_th_E = params.get('V_th_E')  # threshold voltage
-    V_th_I = params.get('V_th_I')
-    tau_E = params.get('tau_E')
-    tau_I = params.get('tau_I')
-    E_L = params.get('E_L')
-    neuron_type = params.get('neuron_type')
-    if ('iaf_psc_exp' in neuron_type) or ('gif_psc_exp' in neuron_type):
-        tau_syn_ex = params.get('tau_syn_ex')
-        tau_syn_in = params.get('tau_syn_in')
+    ps = params.get("ps")  # connection probs
+    ge = params.get("ge")
+    gi = params.get("gi")
+    gie = params.get("gie")
+    V_th_E = params.get("V_th_E")  # threshold voltage
+    V_th_I = params.get("V_th_I")
+    tau_E = params.get("tau_E")
+    tau_I = params.get("tau_I")
+    E_L = params.get("E_L")
+    neuron_type = params.get("neuron_type")
+    if ("iaf_psc_exp" in neuron_type) or ("gif_psc_exp" in neuron_type):
+        tau_syn_ex = params.get("tau_syn_ex")
+        tau_syn_in = params.get("tau_syn_in")
         amp_EE = max_psp_exp(tau_E, tau_syn_ex)
         amp_EI = max_psp_exp(tau_E, tau_syn_in)
         amp_IE = max_psp_exp(tau_I, tau_syn_ex)
         amp_II = max_psp_exp(tau_I, tau_syn_in)
     else:
-        amp_EE = 1.
-        amp_EI = 1.
-        amp_IE = 1.
-        amp_II = 1.
+        amp_EE = 1.0
+        amp_EI = 1.0
+        amp_IE = 1.0
+        amp_II = 1.0
 
     js = np.zeros((2, 2))
     K_EE = N_E * ps[0, 0]
-    js[0, 0] = (V_th_E - E_L) * (K_EE ** -0.5) * N ** 0.5 / amp_EE
+    js[0, 0] = (V_th_E - E_L) * (K_EE**-0.5) * N**0.5 / amp_EE
     js[0, 1] = -ge * js[0, 0] * ps[0, 0] * N_E * amp_EE / (ps[0, 1] * N_I * amp_EI)
     K_IE = N_E * ps[1, 0]
-    js[1, 0] = gie * (V_th_I - E_L) * (K_IE ** -0.5) * N ** 0.5 / amp_IE
+    js[1, 0] = gie * (V_th_I - E_L) * (K_IE**-0.5) * N**0.5 / amp_IE
     js[1, 1] = -gi * js[1, 0] * ps[1, 0] * N_E * amp_IE / (ps[1, 1] * N_I * amp_II)
     return js
 
@@ -98,12 +98,15 @@ def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
     :param vstart: start voltage [mV]
     :return: first passage time [ms]
     """
-    inner = (vtarget - e_l - tau_m * i_e / c_m) / (vstart - e_l - tau_m * i_e / c_m + small)
+    inner = (vtarget - e_l - tau_m * i_e / c_m) / (
+        vstart - e_l - tau_m * i_e / c_m + small
+    )
     if inner < 0:
         return np.nan
     else:
         return -tau_m * np.log(inner)
 
+
 def v_fpt(tau_m, e_l, i_e, c_m, ttarget, vtarget, t_ref):
     """
     Calculate the initial voltage required to obtain a certain first passage time.
@@ -116,4 +119,8 @@ def v_fpt(tau_m, e_l, i_e, c_m, ttarget, vtarget, t_ref):
     :param t_ref: refractory period [ms]
     :return: initial voltage [mV]
     """
-    return (vtarget - e_l - tau_m * i_e / c_m) * np.exp(ttarget / tau_m) + e_l + tau_m * i_e / c_m
+    return (
+        (vtarget - e_l - tau_m * i_e / c_m) * np.exp(ttarget / tau_m)
+        + e_l
+        + tau_m * i_e / c_m
+    )
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 4a8bf1a361..8ea0adbb9d 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -29,14 +29,15 @@
 
 import pickle
 
+import numpy as np
+
 import general_helper
 import helper
 import nest
-import numpy as np
 
 
 class ClusteredNetwork(object):
-    """ Provides functions to create neuron populations, stimulation devices and recording devices for an
+    """Provides functions to create neuron populations, stimulation devices and recording devices for an
     EI-clustered network and setups the simulation in NEST (v3.x).
 
     Creates an object with functions to create neuron populations,
@@ -55,12 +56,17 @@ def __init__(self, defaultValues, parameters):
         self.Populations = []
         self.RecordingDevices = []
         self.Currentsources = []
-        self.ModelBuildPipeline = [self.setup_nest, self.create_populations, self.create_stimulation,
-                                   self.create_recording_devices, self.connect]
+        self.ModelBuildPipeline = [
+            self.setup_nest,
+            self.create_populations,
+            self.create_stimulation,
+            self.create_recording_devices,
+            self.connect,
+        ]
 
     @classmethod
     def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
-        """ Creates a ClusteredNetwork object with parameters Rj and Rep
+        """Creates a ClusteredNetwork object with parameters Rj and Rep
 
         Rj and Rep are used to calculate the parameters jplus (weight clustering) or pplus (probability clustering)
 
@@ -74,21 +80,20 @@ def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
             object: ClusteredNetwork object with parameters
         """
         params = general_helper.merge_params(parameters, defaultValues)
-        if params['clustering'] == 'weight':
+        if params["clustering"] == "weight":
             jep = Rep
-            jip = 1. + (jep - 1) * Rj
-            params['jplus'] = np.array([[jep, jip], [jip, jip]])
-        elif params['clustering'] == 'probabilities':
+            jip = 1.0 + (jep - 1) * Rj
+            params["jplus"] = np.array([[jep, jip], [jip, jip]])
+        elif params["clustering"] == "probabilities":
             pep = Rep
-            pip = 1. + (pep - 1) * Rj
-            params['pplus'] = np.array([[pep, pip], [pip, pip]])
+            pip = 1.0 + (pep - 1) * Rj
+            params["pplus"] = np.array([[pep, pip], [pip, pip]])
         else:
             raise ValueError("Clustering type not recognized")
         return cls(defaultValues, params)
 
-
     def clean_network(self):
-        """ Creates clean network
+        """Creates clean network
 
         Creates empty attributes of a network.
         """
@@ -97,426 +102,674 @@ def clean_network(self):
         self.Currentsources = []
 
     def setup_nest(self):
-        """ Initializes the NEST kernel.
+        """Initializes the NEST kernel.
 
         Reset the NEST kernel and pass parameters to it.
         Updates randseed of parameters to the actual used one if none is supplied.
         """
         nest.ResetKernel()
-        nest.set_verbosity('M_WARNING')
-        nest.local_num_threads = self.params.get('n_jobs', 1)
-        nest.resolution = self.params.get('dt')
-        self.params['randseed'] = self.params.get('randseed', np.random.randint(1000000))
-        nest.rng_seed = self.params.get('randseed')
-        #nest.print_time = True
+        nest.set_verbosity("M_WARNING")
+        nest.local_num_threads = self.params.get("n_jobs", 1)
+        nest.resolution = self.params.get("dt")
+        self.params["randseed"] = self.params.get(
+            "randseed", np.random.randint(1000000)
+        )
+        nest.rng_seed = self.params.get("randseed")
+        # nest.print_time = True
 
     def create_populations(self):
-        """ Create all neuron populations.
+        """Create all neuron populations.
 
         Q excitatory and inhibitory neuron populations with the parameters of the network are created.
 
         """
         # make sure number of clusters and units are compatible
-        assert self.params['N_E'] % self.params['Q'] == 0, 'N_E needs to be evenly divisible by Q'
-        assert self.params['N_I'] % self.params['Q'] == 0, 'N_I needs to be evenly divisible by Q'
-
-        N = self.params['N_E'] + self.params['N_I']  # total units
+        assert (
+            self.params["N_E"] % self.params["Q"] == 0
+        ), "N_E needs to be evenly divisible by Q"
+        assert (
+            self.params["N_I"] % self.params["Q"] == 0
+        ), "N_I needs to be evenly divisible by Q"
+
+        N = self.params["N_E"] + self.params["N_I"]  # total units
         try:
-            DistParams = self.params['DistParams']
+            DistParams = self.params["DistParams"]
         except AttributeError:
-            DistParams = {'distribution': 'normal', 'sigma': 0.0, 'fraction': False}
+            DistParams = {"distribution": "normal", "sigma": 0.0, "fraction": False}
 
-        if self.params['I_th_E'] is None:
-            I_xE = self.params['I_xE']
+        if self.params["I_th_E"] is None:
+            I_xE = self.params["I_xE"]
         else:
-            I_xE = self.params['I_th_E'] * (self.params['V_th_E'] - self.params['E_L']) / self.params['tau_E'] * \
-                   self.params['C_m']
-
-        if self.params['I_th_I'] is None:
-            I_xI = self.params['I_xI']
+            I_xE = (
+                self.params["I_th_E"]
+                * (self.params["V_th_E"] - self.params["E_L"])
+                / self.params["tau_E"]
+                * self.params["C_m"]
+            )
+
+        if self.params["I_th_I"] is None:
+            I_xI = self.params["I_xI"]
         else:
-            I_xI = self.params['I_th_I'] * (self.params['V_th_I'] - self.params['E_L']) / self.params['tau_I'] * \
-                   self.params['C_m']
-
-        E_neuron_params = {'E_L': self.params['E_L'], 'C_m': self.params['C_m'], 'tau_m': self.params['tau_E'],
-                           't_ref': self.params['t_ref'], 'V_th': self.params['V_th_E'], 'V_reset': self.params['V_r'],
-                           'I_e': I_xE}
-        I_neuron_params = {'E_L': self.params['E_L'], 'C_m': self.params['C_m'], 'tau_m': self.params['tau_I'],
-                           't_ref': self.params['t_ref'], 'V_th': self.params['V_th_I'], 'V_reset': self.params['V_r'],
-                           'I_e': I_xI}
-        if 'iaf_psc_exp' in self.params['neuron_type']:
-            E_neuron_params['tau_syn_ex'] = self.params['tau_syn_ex']
-            E_neuron_params['tau_syn_in'] = self.params['tau_syn_in']
-            I_neuron_params['tau_syn_in'] = self.params['tau_syn_in']
-            I_neuron_params['tau_syn_ex'] = self.params['tau_syn_ex']
+            I_xI = (
+                self.params["I_th_I"]
+                * (self.params["V_th_I"] - self.params["E_L"])
+                / self.params["tau_I"]
+                * self.params["C_m"]
+            )
+
+        E_neuron_params = {
+            "E_L": self.params["E_L"],
+            "C_m": self.params["C_m"],
+            "tau_m": self.params["tau_E"],
+            "t_ref": self.params["t_ref"],
+            "V_th": self.params["V_th_E"],
+            "V_reset": self.params["V_r"],
+            "I_e": I_xE,
+        }
+        I_neuron_params = {
+            "E_L": self.params["E_L"],
+            "C_m": self.params["C_m"],
+            "tau_m": self.params["tau_I"],
+            "t_ref": self.params["t_ref"],
+            "V_th": self.params["V_th_I"],
+            "V_reset": self.params["V_r"],
+            "I_e": I_xI,
+        }
+        if "iaf_psc_exp" in self.params["neuron_type"]:
+            E_neuron_params["tau_syn_ex"] = self.params["tau_syn_ex"]
+            E_neuron_params["tau_syn_in"] = self.params["tau_syn_in"]
+            I_neuron_params["tau_syn_in"] = self.params["tau_syn_in"]
+            I_neuron_params["tau_syn_ex"] = self.params["tau_syn_ex"]
 
             # iaf_psc_exp allows stochasticity, if not used - ignore
             try:
-                if self.params['delta_'] is not None:
-                    E_neuron_params['delta'] = self.params['delta_']
-                    I_neuron_params['delta'] = self.params['delta_']
-                if self.params['rho'] is not None:
-                    E_neuron_params['rho'] = self.params['rho']
-                    I_neuron_params['rho'] = self.params['rho']
+                if self.params["delta_"] is not None:
+                    E_neuron_params["delta"] = self.params["delta_"]
+                    I_neuron_params["delta"] = self.params["delta_"]
+                if self.params["rho"] is not None:
+                    E_neuron_params["rho"] = self.params["rho"]
+                    I_neuron_params["rho"] = self.params["rho"]
             except KeyError:
                 pass
         else:
-            assert 'iaf_psc_exp' in self.params['neuron_type'], "iaf_psc_exp neuron model is the only implemented model"
+            assert (
+                "iaf_psc_exp" in self.params["neuron_type"]
+            ), "iaf_psc_exp neuron model is the only implemented model"
 
             # create the neuron populations
         E_pops = []
         I_pops = []
-        for q in range(self.params['Q']):
-            E_pops.append(nest.Create(self.params['neuron_type'], int(self.params['N_E'] / self.params['Q'])))
+        for q in range(self.params["Q"]):
+            E_pops.append(
+                nest.Create(
+                    self.params["neuron_type"],
+                    int(self.params["N_E"] / self.params["Q"]),
+                )
+            )
             nest.SetStatus(E_pops[-1], E_neuron_params)
-        for q in range(self.params['Q']):
-            I_pops.append(nest.Create(self.params['neuron_type'], int(self.params['N_I'] / self.params['Q'])))
+        for q in range(self.params["Q"]):
+            I_pops.append(
+                nest.Create(
+                    self.params["neuron_type"],
+                    int(self.params["N_I"] / self.params["Q"]),
+                )
+            )
             nest.SetStatus(I_pops[-1], I_neuron_params)
 
-        if self.params['delta_I_xE'] > 0:
+        if self.params["delta_I_xE"] > 0:
             for E_pop in E_pops:
-                I_xEs = nest.GetStatus(E_pop, 'I_e')
-                nest.SetStatus(E_pop, [
-                    {'I_e': (1 - 0.5 * self.params['delta_I_xE'] + np.random.rand() * self.params['delta_I_xE']) * ixe}
-                    for ixe in I_xEs])
-
-        if self.params['delta_I_xI'] > 0:
+                I_xEs = nest.GetStatus(E_pop, "I_e")
+                nest.SetStatus(
+                    E_pop,
+                    [
+                        {
+                            "I_e": (
+                                1
+                                - 0.5 * self.params["delta_I_xE"]
+                                + np.random.rand() * self.params["delta_I_xE"]
+                            )
+                            * ixe
+                        }
+                        for ixe in I_xEs
+                    ],
+                )
+
+        if self.params["delta_I_xI"] > 0:
             for I_pop in I_pops:
-                I_xIs = nest.GetStatus(I_pop, 'I_e')
-                nest.SetStatus(I_pop, [
-                    {'I_e': (1 - 0.5 * self.params['delta_I_xI'] + np.random.rand() * self.params['delta_I_xI']) * ixi}
-                    for ixi in I_xIs])
-        if self.params['V_m'] == 'rand':
-            T_0_E = self.params['t_ref'] + helper.fpt(self.params['tau_E'], self.params['E_L'], I_xE,
-                                                      self.params['C_m'], self.params['V_th_E'],
-                                                      self.params['V_r'])
+                I_xIs = nest.GetStatus(I_pop, "I_e")
+                nest.SetStatus(
+                    I_pop,
+                    [
+                        {
+                            "I_e": (
+                                1
+                                - 0.5 * self.params["delta_I_xI"]
+                                + np.random.rand() * self.params["delta_I_xI"]
+                            )
+                            * ixi
+                        }
+                        for ixi in I_xIs
+                    ],
+                )
+        if self.params["V_m"] == "rand":
+            T_0_E = self.params["t_ref"] + helper.fpt(
+                self.params["tau_E"],
+                self.params["E_L"],
+                I_xE,
+                self.params["C_m"],
+                self.params["V_th_E"],
+                self.params["V_r"],
+            )
             if np.isnan(T_0_E):
-                T_0_E = 10.
+                T_0_E = 10.0
             for E_pop in E_pops:
-                nest.SetStatus(E_pop, [{'V_m': helper.v_fpt(self.params['tau_E'], self.params['E_L'], I_xE,
-                                                            self.params['C_m'], T_0_E * np.random.rand(),
-                                                            self.params['V_th_E'], self.params['t_ref'])} for i
-                                       in range(len(E_pop))])
-
-            T_0_I = self.params['t_ref'] + helper.fpt(self.params['tau_I'], self.params['E_L'], I_xI,
-                                                      self.params['C_m'], self.params['V_th_I'],
-                                                      self.params['V_r'])
+                nest.SetStatus(
+                    E_pop,
+                    [
+                        {
+                            "V_m": helper.v_fpt(
+                                self.params["tau_E"],
+                                self.params["E_L"],
+                                I_xE,
+                                self.params["C_m"],
+                                T_0_E * np.random.rand(),
+                                self.params["V_th_E"],
+                                self.params["t_ref"],
+                            )
+                        }
+                        for i in range(len(E_pop))
+                    ],
+                )
+
+            T_0_I = self.params["t_ref"] + helper.fpt(
+                self.params["tau_I"],
+                self.params["E_L"],
+                I_xI,
+                self.params["C_m"],
+                self.params["V_th_I"],
+                self.params["V_r"],
+            )
             if np.isnan(T_0_I):
-                T_0_I = 10.
+                T_0_I = 10.0
             for I_pop in I_pops:
-                nest.SetStatus(I_pop, [{'V_m': helper.v_fpt(self.params['tau_I'], self.params['E_L'], I_xI,
-                                                            self.params['C_m'], T_0_I * np.random.rand(),
-                                                            self.params['V_th_E'], self.params['t_ref'])} for i
-                                       in range(len(I_pop))])
+                nest.SetStatus(
+                    I_pop,
+                    [
+                        {
+                            "V_m": helper.v_fpt(
+                                self.params["tau_I"],
+                                self.params["E_L"],
+                                I_xI,
+                                self.params["C_m"],
+                                T_0_I * np.random.rand(),
+                                self.params["V_th_E"],
+                                self.params["t_ref"],
+                            )
+                        }
+                        for i in range(len(I_pop))
+                    ],
+                )
         else:
-            nest.SetStatus(nest.NodeCollection([x for x in range(1, N + 1)]),
-                           [{'V_m': self.params['V_m']} for i in range(N)])
+            nest.SetStatus(
+                nest.NodeCollection([x for x in range(1, N + 1)]),
+                [{"V_m": self.params["V_m"]} for i in range(N)],
+            )
         self.Populations = [E_pops, I_pops]
 
-
     def connect(self):
         """
         Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
         """
         # if CLustering is set to "Weight" or is not set at all or is not in dictionary, use the connect_weight function
         try:
-            if self.params['clustering'] == 'weight':
+            if self.params["clustering"] == "weight":
                 self.connect_weight()
-            elif self.params['clustering'] == 'probabilities':
+            elif self.params["clustering"] == "probabilities":
                 self.connect_probabilities()
             else:
-                raise ValueError('Clustering method %s not implemented' % self.params['clustering'])
+                raise ValueError(
+                    "Clustering method %s not implemented" % self.params["clustering"]
+                )
         except KeyError:
             self.connect_weight()
 
     def connect_probabilities(self):
-        """ Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
-            by increasing the probabilies of the connections within the clusters and decreasing the probabilites of the
-            connections between the clusters. The weights are calculated so that the total input to a neuron
-            is balanced.
+        """Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        by increasing the probabilies of the connections within the clusters and decreasing the probabilites of the
+        connections between the clusters. The weights are calculated so that the total input to a neuron
+        is balanced.
         """
         #  self.Populations[0] -> Excitatory population
         #  self.Populations[1] -> Inhibitory population
         # connectivity parameters
-        js = self.params['js']  # connection weights
-        N = self.params['N_E'] + self.params['N_I']  # total units
+        js = self.params["js"]  # connection weights
+        N = self.params["N_E"] + self.params["N_I"]  # total units
         if np.isnan(js).any():
             js = helper.calc_js(self.params)
-        js *= self.params['s']
+        js *= self.params["s"]
 
-        if self.params['Q'] > 1:
-            pminus = (self.params['Q'] - self.params['pplus']) / float(self.params['Q'] - 1)
+        if self.params["Q"] > 1:
+            pminus = (self.params["Q"] - self.params["pplus"]) / float(
+                self.params["Q"] - 1
+            )
         else:
-            self.params['pplus'] = np.ones((2, 2))
+            self.params["pplus"] = np.ones((2, 2))
             pminus = np.ones((2, 2))
 
-        p_plus= self.params['pplus']*self.params['ps']
-        p_minus = pminus*self.params['ps']
+        p_plus = self.params["pplus"] * self.params["ps"]
+        p_minus = pminus * self.params["ps"]
 
         iterations = np.ones((2, 2), dtype=int)
         # test if any of the probabilities is larger than 1
         if np.any(p_plus > 1):
-            print('The probability of connection is larger than 1')
-            print('p_plus: ', p_plus)
-            print('p_minus: ', p_minus)
+            print("The probability of connection is larger than 1")
+            print("p_plus: ", p_plus)
+            print("p_minus: ", p_minus)
             for i in range(2):
                 for j in range(2):
                     if p_plus[i, j] > 1:
                         iterations[i, j] = int(np.ceil(p_plus[i, j]))
                         p_plus[i, j] /= iterations[i, j]
-            print('p_plus: ', p_plus)
+            print("p_plus: ", p_plus)
             print(iterations)
 
         # define the synapses and connect the populations
         # EE
         j_ee = js[0, 0] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "EE",
-                       {"weight": j_ee, "delay": self.params['delay']})
-
-
-        if self.params['fixed_indegree']:
-            K_EE_plus = int(p_plus[0, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_EE+: ', K_EE_plus)
-            K_EE_minus = int(p_minus[0, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_EE-: ', K_EE_minus)
-            conn_params_EE_plus = {'rule': 'fixed_indegree', 'indegree': K_EE_plus, 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_EE_minus = {'rule': 'fixed_indegree', 'indegree': K_EE_minus, 'allow_autapses': False,
-                                     'allow_multapses': True}
+        nest.CopyModel(
+            "static_synapse", "EE", {"weight": j_ee, "delay": self.params["delay"]}
+        )
+
+        if self.params["fixed_indegree"]:
+            K_EE_plus = int(p_plus[0, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_EE+: ", K_EE_plus)
+            K_EE_minus = int(p_minus[0, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_EE-: ", K_EE_minus)
+            conn_params_EE_plus = {
+                "rule": "fixed_indegree",
+                "indegree": K_EE_plus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_EE_minus = {
+                "rule": "fixed_indegree",
+                "indegree": K_EE_minus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
 
         else:
-            conn_params_EE_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[0, 0], 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_EE_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[0, 0], 'allow_autapses': False,
-                                   'allow_multapses': True}
+            conn_params_EE_plus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_plus[0, 0],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_EE_minus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_minus[0, 0],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
         for i, pre in enumerate(self.Populations[0]):
             for j, post in enumerate(self.Populations[0]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[0, 0]):
-                        nest.Connect(pre, post, conn_params_EE_plus, 'EE')
+                        nest.Connect(pre, post, conn_params_EE_plus, "EE")
                 else:
-                    nest.Connect(pre, post, conn_params_EE_minus, 'EE')
+                    nest.Connect(pre, post, conn_params_EE_minus, "EE")
 
         # EI
         j_ei = js[0, 1] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "EI",
-                       {"weight": j_ei, "delay": self.params['delay']})
-
-        if self.params['fixed_indegree']:
-            K_EI_plus = int(p_plus[0, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_EI+: ', K_EI_plus)
-            K_EI_minus = int(p_minus[0, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_EI-: ', K_EI_minus)
-            conn_params_EI_plus = {'rule': 'fixed_indegree', 'indegree': K_EI_plus, 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_EI_minus = {'rule': 'fixed_indegree', 'indegree': K_EI_minus, 'allow_autapses': False,
-                                     'allow_multapses': True}
+        nest.CopyModel(
+            "static_synapse", "EI", {"weight": j_ei, "delay": self.params["delay"]}
+        )
+
+        if self.params["fixed_indegree"]:
+            K_EI_plus = int(p_plus[0, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_EI+: ", K_EI_plus)
+            K_EI_minus = int(p_minus[0, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_EI-: ", K_EI_minus)
+            conn_params_EI_plus = {
+                "rule": "fixed_indegree",
+                "indegree": K_EI_plus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_EI_minus = {
+                "rule": "fixed_indegree",
+                "indegree": K_EI_minus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
 
         else:
-            conn_params_EI_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[0, 1], 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_EI_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[0, 1], 'allow_autapses': False,
-                                   'allow_multapses': True}
+            conn_params_EI_plus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_plus[0, 1],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_EI_minus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_minus[0, 1],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
         for i, pre in enumerate(self.Populations[1]):
             for j, post in enumerate(self.Populations[0]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[0, 1]):
-                        nest.Connect(pre, post, conn_params_EI_plus, 'EI')
+                        nest.Connect(pre, post, conn_params_EI_plus, "EI")
                 else:
-                    nest.Connect(pre, post, conn_params_EI_minus, 'EI')
+                    nest.Connect(pre, post, conn_params_EI_minus, "EI")
 
         # IE
         j_ie = js[1, 0] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "IE",
-                          {"weight": j_ie, "delay": self.params['delay']})
-
-        if self.params['fixed_indegree']:
-            K_IE_plus = int(p_plus[1, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_IE+: ', K_IE_plus)
-            K_IE_minus = int(p_minus[1, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_IE-: ', K_IE_minus)
-            conn_params_IE_plus = {'rule': 'fixed_indegree', 'indegree': K_IE_plus, 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_IE_minus = {'rule': 'fixed_indegree', 'indegree': K_IE_minus, 'allow_autapses': False,
-                                     'allow_multapses': True}
+        nest.CopyModel(
+            "static_synapse", "IE", {"weight": j_ie, "delay": self.params["delay"]}
+        )
+
+        if self.params["fixed_indegree"]:
+            K_IE_plus = int(p_plus[1, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_IE+: ", K_IE_plus)
+            K_IE_minus = int(p_minus[1, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_IE-: ", K_IE_minus)
+            conn_params_IE_plus = {
+                "rule": "fixed_indegree",
+                "indegree": K_IE_plus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_IE_minus = {
+                "rule": "fixed_indegree",
+                "indegree": K_IE_minus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
 
         else:
-            conn_params_IE_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[1, 0], 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_IE_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[1, 0], 'allow_autapses': False,
-                                   'allow_multapses': True}
+            conn_params_IE_plus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_plus[1, 0],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_IE_minus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_minus[1, 0],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
         for i, pre in enumerate(self.Populations[0]):
             for j, post in enumerate(self.Populations[1]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[1, 0]):
-                        nest.Connect(pre, post, conn_params_IE_plus, 'IE')
+                        nest.Connect(pre, post, conn_params_IE_plus, "IE")
                 else:
-                    nest.Connect(pre, post, conn_params_IE_minus, 'IE')
+                    nest.Connect(pre, post, conn_params_IE_minus, "IE")
 
         # II
         j_ii = js[1, 1] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "II",
-                            {"weight": j_ii, "delay": self.params['delay']})
-
-        if self.params['fixed_indegree']:
-            K_II_plus = int(p_plus[1, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_II+: ', K_II_plus)
-            K_II_minus = int(p_minus[1, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_II-: ', K_II_minus)
-            conn_params_II_plus = {'rule': 'fixed_indegree', 'indegree': K_II_plus, 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_II_minus = {'rule': 'fixed_indegree', 'indegree': K_II_minus, 'allow_autapses': False,
-                                     'allow_multapses': True}
+        nest.CopyModel(
+            "static_synapse", "II", {"weight": j_ii, "delay": self.params["delay"]}
+        )
+
+        if self.params["fixed_indegree"]:
+            K_II_plus = int(p_plus[1, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_II+: ", K_II_plus)
+            K_II_minus = int(p_minus[1, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_II-: ", K_II_minus)
+            conn_params_II_plus = {
+                "rule": "fixed_indegree",
+                "indegree": K_II_plus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_II_minus = {
+                "rule": "fixed_indegree",
+                "indegree": K_II_minus,
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
 
         else:
-            conn_params_II_plus = {'rule': 'pairwise_bernoulli', 'p': p_plus[1, 1], 'allow_autapses': False,
-                              'allow_multapses': True}
-            conn_params_II_minus = {'rule': 'pairwise_bernoulli', 'p': p_minus[1, 1], 'allow_autapses': False,
-                                   'allow_multapses': True}
+            conn_params_II_plus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_plus[1, 1],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
+            conn_params_II_minus = {
+                "rule": "pairwise_bernoulli",
+                "p": p_minus[1, 1],
+                "allow_autapses": False,
+                "allow_multapses": True,
+            }
         for i, pre in enumerate(self.Populations[1]):
             for j, post in enumerate(self.Populations[1]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[1, 1]):
-                        nest.Connect(pre, post, conn_params_II_plus, 'II')
+                        nest.Connect(pre, post, conn_params_II_plus, "II")
                 else:
-                    nest.Connect(pre, post, conn_params_II_minus, 'II')
-
+                    nest.Connect(pre, post, conn_params_II_minus, "II")
 
     def connect_weight(self):
-        """ Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
-            by increasing the weights of the connections within the clusters and decreasing the weights of the
-            connections between the clusters. The weights are calculated so that the total input to a neuron
-            is balanced.
+        """Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        by increasing the weights of the connections within the clusters and decreasing the weights of the
+        connections between the clusters. The weights are calculated so that the total input to a neuron
+        is balanced.
         """
         #  self.Populations[0] -> Excitatory population
         #  self.Populations[1] -> Inhibitory population
         # connectivity parameters
-        js = self.params['js']  # connection weights
-        N = self.params['N_E'] + self.params['N_I']  # total units
+        js = self.params["js"]  # connection weights
+        N = self.params["N_E"] + self.params["N_I"]  # total units
 
         # if js are not given compute them so that sqrt(K) spikes equal v_thr-E_L and rows are balanced
         if np.isnan(js).any():
             js = helper.calc_js(self.params)
-        js *= self.params['s']
+        js *= self.params["s"]
 
         # jminus is calculated so that row sums remain constant
-        if self.params['Q'] > 1:
-            jminus = (self.params['Q'] - self.params['jplus']) / float(self.params['Q'] - 1)
+        if self.params["Q"] > 1:
+            jminus = (self.params["Q"] - self.params["jplus"]) / float(
+                self.params["Q"] - 1
+            )
         else:
-            self.params['jplus'] = np.ones((2, 2))
+            self.params["jplus"] = np.ones((2, 2))
             jminus = np.ones((2, 2))
 
         # define the synapses and connect the populations
         # EE
         j_ee = js[0, 0] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "EE_plus",
-                       {"weight": self.params['jplus'][0, 0] * j_ee, "delay": self.params['delay']})
-        nest.CopyModel("static_synapse", "EE_minus", {"weight": jminus[0, 0] * j_ee, "delay": self.params['delay']})
-        if self.params['fixed_indegree']:
-            K_EE = int(self.params['ps'][0, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_EE: ', K_EE)
-            conn_params_EE = {'rule': 'fixed_indegree', 'indegree': K_EE, 'allow_autapses': False,
-                              'allow_multapses': False}
+        nest.CopyModel(
+            "static_synapse",
+            "EE_plus",
+            {
+                "weight": self.params["jplus"][0, 0] * j_ee,
+                "delay": self.params["delay"],
+            },
+        )
+        nest.CopyModel(
+            "static_synapse",
+            "EE_minus",
+            {"weight": jminus[0, 0] * j_ee, "delay": self.params["delay"]},
+        )
+        if self.params["fixed_indegree"]:
+            K_EE = int(self.params["ps"][0, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_EE: ", K_EE)
+            conn_params_EE = {
+                "rule": "fixed_indegree",
+                "indegree": K_EE,
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
 
         else:
-            conn_params_EE = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][0, 0], 'allow_autapses': False,
-                              'allow_multapses': False}
+            conn_params_EE = {
+                "rule": "pairwise_bernoulli",
+                "p": self.params["ps"][0, 0],
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         for i, pre in enumerate(self.Populations[0]):
             for j, post in enumerate(self.Populations[0]):
                 if i == j:
                     # same cluster
-                    nest.Connect(pre, post, conn_params_EE, 'EE_plus')
+                    nest.Connect(pre, post, conn_params_EE, "EE_plus")
                 else:
-                    nest.Connect(pre, post, conn_params_EE, 'EE_minus')
+                    nest.Connect(pre, post, conn_params_EE, "EE_minus")
 
         # EI
         j_ei = js[0, 1] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "EI_plus",
-                       {"weight": j_ei * self.params['jplus'][0, 1], "delay": self.params['delay']})
-        nest.CopyModel("static_synapse", "EI_minus", {"weight": j_ei * jminus[0, 1], "delay": self.params['delay']})
-        if self.params['fixed_indegree']:
-            K_EI = int(self.params['ps'][0, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_EI: ', K_EI)
-            conn_params_EI = {'rule': 'fixed_indegree', 'indegree': K_EI, 'allow_autapses': False,
-                              'allow_multapses': False}
+        nest.CopyModel(
+            "static_synapse",
+            "EI_plus",
+            {
+                "weight": j_ei * self.params["jplus"][0, 1],
+                "delay": self.params["delay"],
+            },
+        )
+        nest.CopyModel(
+            "static_synapse",
+            "EI_minus",
+            {"weight": j_ei * jminus[0, 1], "delay": self.params["delay"]},
+        )
+        if self.params["fixed_indegree"]:
+            K_EI = int(self.params["ps"][0, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_EI: ", K_EI)
+            conn_params_EI = {
+                "rule": "fixed_indegree",
+                "indegree": K_EI,
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         else:
-            conn_params_EI = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][0, 1], 'allow_autapses': False,
-                              'allow_multapses': False}
+            conn_params_EI = {
+                "rule": "pairwise_bernoulli",
+                "p": self.params["ps"][0, 1],
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         for i, pre in enumerate(self.Populations[1]):
             for j, post in enumerate(self.Populations[0]):
                 if i == j:
                     # same cluster
-                    nest.Connect(pre, post, conn_params_EI, 'EI_plus')
+                    nest.Connect(pre, post, conn_params_EI, "EI_plus")
                 else:
-                    nest.Connect(pre, post, conn_params_EI, 'EI_minus')
+                    nest.Connect(pre, post, conn_params_EI, "EI_minus")
         # IE
         j_ie = js[1, 0] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "IE_plus",
-                       {"weight": j_ie * self.params['jplus'][1, 0], "delay": self.params['delay']})
-        nest.CopyModel("static_synapse", "IE_minus", {"weight": j_ie * jminus[1, 0], "delay": self.params['delay']})
-
-        if self.params['fixed_indegree']:
-            K_IE = int(self.params['ps'][1, 0] * self.params['N_E'] / self.params['Q'])
-            print('K_IE: ', K_IE)
-            conn_params_IE = {'rule': 'fixed_indegree', 'indegree': K_IE, 'allow_autapses': False,
-                              'allow_multapses': False}
+        nest.CopyModel(
+            "static_synapse",
+            "IE_plus",
+            {
+                "weight": j_ie * self.params["jplus"][1, 0],
+                "delay": self.params["delay"],
+            },
+        )
+        nest.CopyModel(
+            "static_synapse",
+            "IE_minus",
+            {"weight": j_ie * jminus[1, 0], "delay": self.params["delay"]},
+        )
+
+        if self.params["fixed_indegree"]:
+            K_IE = int(self.params["ps"][1, 0] * self.params["N_E"] / self.params["Q"])
+            print("K_IE: ", K_IE)
+            conn_params_IE = {
+                "rule": "fixed_indegree",
+                "indegree": K_IE,
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         else:
-            conn_params_IE = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][1, 0], 'allow_autapses': False,
-                              'allow_multapses': False}
+            conn_params_IE = {
+                "rule": "pairwise_bernoulli",
+                "p": self.params["ps"][1, 0],
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         for i, pre in enumerate(self.Populations[0]):
             for j, post in enumerate(self.Populations[1]):
                 if i == j:
                     # same cluster
-                    nest.Connect(pre, post, conn_params_IE, 'IE_plus')
+                    nest.Connect(pre, post, conn_params_IE, "IE_plus")
                 else:
-                    nest.Connect(pre, post, conn_params_IE, 'IE_minus')
+                    nest.Connect(pre, post, conn_params_IE, "IE_minus")
 
         # II
         j_ii = js[1, 1] / np.sqrt(N)
-        nest.CopyModel("static_synapse", "II_plus",
-                       {"weight": j_ii * self.params['jplus'][1, 1], "delay": self.params['delay']})
-        nest.CopyModel("static_synapse", "II_minus", {"weight": j_ii * jminus[1, 1], "delay": self.params['delay']})
-        if self.params['fixed_indegree']:
-            K_II = int(self.params['ps'][1, 1] * self.params['N_I'] / self.params['Q'])
-            print('K_II: ', K_II)
-            conn_params_II = {'rule': 'fixed_indegree', 'indegree': K_II, 'allow_autapses': False,
-                              'allow_multapses': False}
+        nest.CopyModel(
+            "static_synapse",
+            "II_plus",
+            {
+                "weight": j_ii * self.params["jplus"][1, 1],
+                "delay": self.params["delay"],
+            },
+        )
+        nest.CopyModel(
+            "static_synapse",
+            "II_minus",
+            {"weight": j_ii * jminus[1, 1], "delay": self.params["delay"]},
+        )
+        if self.params["fixed_indegree"]:
+            K_II = int(self.params["ps"][1, 1] * self.params["N_I"] / self.params["Q"])
+            print("K_II: ", K_II)
+            conn_params_II = {
+                "rule": "fixed_indegree",
+                "indegree": K_II,
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         else:
-            conn_params_II = {'rule': 'pairwise_bernoulli', 'p': self.params['ps'][1, 1], 'allow_autapses': False,
-                              'allow_multapses': False}
+            conn_params_II = {
+                "rule": "pairwise_bernoulli",
+                "p": self.params["ps"][1, 1],
+                "allow_autapses": False,
+                "allow_multapses": False,
+            }
         for i, pre in enumerate(self.Populations[1]):
             for j, post in enumerate(self.Populations[1]):
                 if i == j:
                     # same cluster
-                    nest.Connect(pre, post, conn_params_II, 'II_plus')
+                    nest.Connect(pre, post, conn_params_II, "II_plus")
                 else:
-                    nest.Connect(pre, post, conn_params_II, 'II_minus')
-        #print('Js: ', js / np.sqrt(N))
+                    nest.Connect(pre, post, conn_params_II, "II_minus")
+        # print('Js: ', js / np.sqrt(N))
 
     def create_stimulation(self):
         """
         Creates a current source and connects it to the specified cluster/s.
         """
-        if self.params['stim_clusters'] is not None:
-            stim_amp = self.params['stim_amp']  # amplitude of the stimulation current in pA
-            stim_starts = self.params['stim_starts']  # list of stimulation start times
-            stim_ends = self.params['stim_ends']  # list of stimulation end times
+        if self.params["stim_clusters"] is not None:
+            stim_amp = self.params[
+                "stim_amp"
+            ]  # amplitude of the stimulation current in pA
+            stim_starts = self.params["stim_starts"]  # list of stimulation start times
+            stim_ends = self.params["stim_ends"]  # list of stimulation end times
             amplitude_values = []
             amplitude_times = []
             for start, end in zip(stim_starts, stim_ends):
-                amplitude_times.append(start + self.params['warmup'])
+                amplitude_times.append(start + self.params["warmup"])
                 amplitude_values.append(stim_amp)
-                amplitude_times.append(end + self.params['warmup'])
-                amplitude_values.append(0.)
-            self.Currentsources = [nest.Create('step_current_generator')]
-            for stim_cluster in self.params['stim_clusters']:
+                amplitude_times.append(end + self.params["warmup"])
+                amplitude_values.append(0.0)
+            self.Currentsources = [nest.Create("step_current_generator")]
+            for stim_cluster in self.params["stim_clusters"]:
                 nest.Connect(self.Currentsources[0], self.Populations[0][stim_cluster])
-            nest.SetStatus(self.Currentsources[0],
-                           {'amplitude_times': amplitude_times, 'amplitude_values': amplitude_values})
+            nest.SetStatus(
+                self.Currentsources[0],
+                {
+                    "amplitude_times": amplitude_times,
+                    "amplitude_values": amplitude_values,
+                },
+            )
 
     def create_recording_devices(self):
         """
@@ -550,15 +803,14 @@ def setup_network(self):
             func()
         nest.Prepare()
 
-
     def simulate(self):
         """
         Simulates network for a period of warmup+simtime
         """
-        if self.params['warmup'] + self.params['simtime'] <= 0.1:
+        if self.params["warmup"] + self.params["simtime"] <= 0.1:
             pass
         else:
-            nest.Run(self.params['warmup'] + self.params['simtime'])
+            nest.Run(self.params["warmup"] + self.params["simtime"])
 
     def get_recordings(self):
         """
@@ -569,13 +821,15 @@ def get_recordings(self):
         Returns:
             spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
         """
-        events = nest.GetStatus(self.RecordingDevices[0], 'events')[0]
+        events = nest.GetStatus(self.RecordingDevices[0], "events")[0]
         # convert them to the format accepted by spiketools
-        spiketimes = np.append(events['times'][None, :], events['senders'][None, :], axis=0)
+        spiketimes = np.append(
+            events["times"][None, :], events["senders"][None, :], axis=0
+        )
         spiketimes[1] -= 1
         # remove the pre warmup spikes
-        spiketimes = spiketimes[:, spiketimes[0] >= self.params['warmup']]
-        spiketimes[0] -= self.params['warmup']
+        spiketimes = spiketimes[:, spiketimes[0] >= self.params["warmup"]]
+        spiketimes[0] -= self.params["warmup"]
         return spiketimes
 
     def get_parameter(self):
@@ -610,10 +864,14 @@ def get_firing_rates(self, spiketimes=None):
         """
         if spiketimes is None:
             spiketimes = self.get_recordings()
-        e_count = spiketimes[:, spiketimes[1] < self.params['N_E']].shape[1]
-        i_count = spiketimes[:, spiketimes[1] >= self.params['N_E']].shape[1]
-        e_rate = e_count / float(self.params['N_E']) / float(self.params['simtime']) * 1000.
-        i_rate = i_count / float(self.params['N_I']) / float(self.params['simtime']) * 1000.
+        e_count = spiketimes[:, spiketimes[1] < self.params["N_E"]].shape[1]
+        i_count = spiketimes[:, spiketimes[1] >= self.params["N_E"]].shape[1]
+        e_rate = (
+            e_count / float(self.params["N_E"]) / float(self.params["simtime"]) * 1000.0
+        )
+        i_rate = (
+            i_count / float(self.params["N_I"]) / float(self.params["simtime"]) * 1000.0
+        )
         return e_rate, i_rate
 
     def set_I_x(self, I_XE, I_XI):
@@ -622,11 +880,11 @@ def set_I_x(self, I_XE, I_XI):
         model setup.
         """
         for E_pop in self.Populations[0]:
-            I_e_loc = E_pop.get('I_e')
-            E_pop.set({'I_e': I_e_loc + I_XE})
+            I_e_loc = E_pop.get("I_e")
+            E_pop.set({"I_e": I_e_loc + I_XE})
         for I_pop in self.Populations[1]:
-            I_e_loc = I_pop.get('I_e')
-            I_pop.set({'I_e': I_e_loc + I_XI})
+            I_e_loc = I_pop.get("I_e")
+            I_pop.set({"I_e": I_e_loc + I_XI})
 
     def get_simulation(self, PathSpikes=None):
         """
@@ -645,9 +903,13 @@ def get_simulation(self, PathSpikes=None):
         e_rate, i_rate = self.get_firing_rates(spiketimes)
 
         if PathSpikes is not None:
-            with open(PathSpikes, 'wb') as outfile:
+            with open(PathSpikes, "wb") as outfile:
                 pickle.dump(spiketimes, outfile)
 
         nest.Cleanup()
-        return {'e_rate': e_rate, 'i_rate': i_rate, 'params': self.get_parameter(),
-                'spiketimes': spiketimes}
+        return {
+            "e_rate": e_rate,
+            "i_rate": i_rate,
+            "params": self.get_parameter(),
+            "spiketimes": spiketimes,
+        }
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 39f260b12c..1c8479e2c0 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -26,54 +26,64 @@
 and generating a raster plot.
 """
 
+import matplotlib.pyplot as plt
+import numpy as np
+
 import default_parameters as default
 import general_helper
-import matplotlib.pyplot as plt
 import network
-import numpy as np
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # set the parameters for the simulation
-    params = {'n_jobs': 12,  # number of threads used for parallelization in NEST
-              'N_E': 4000,  # number of excitatory neurons
-              'N_I': 1000,  # number of inhibitory neurons
-              'E_L': 0.,  # leak potential
-              'V_th_E': 20.,  # threshold potential for excitatory neurons
-              'V_th_I': 20.,  # threshold potential for inhibitory neurons
-              'V_r': 0.,  # reset potential
-              'C_m': 1.,  # membrane capacity
-              'tau_E': 20.,  # membrane time constant for excitatory neurons
-              'tau_I': 10.,  # membrane time constant for inhibitory neurons
-              't_ref': 5.,  # refractory period
-              'ps': np.array([[0.2, 0.5], [0.5, 0.5]]),  # connection probabilities
-              'ge': 1.2,  # excitatory synaptic weight factor
-              'I_th_E': 1.25,  # Background current for excitatory neurons in multiples of I_rheobase
-              'I_th_I': 0.78,  # Background current for inhibitory neurons in multiples of I_rheobase
-              'dt': 0.1,  # simulation time step
-              'neuron_type': 'iaf_psc_exp',  # neuron model used in NEST iaf and gif are available
-              'simtime': 10000,  # simulation time in ms
-              'warmup': 1000,  # warmup time in ms (time before recording starts)
-              'Q': 20,  # number of clusters
-              'clustering': 'weight',  # type of clustering used 'weight' or
-              'stim_clusters': [2, 3, 4],  # list of clusters which are stimulated
-              'stim_starts': [2000, 6000],  # list of stimulation start times
-              'stim_ends': [3500, 7500],  # list of stimulation end times
-              'stim_amp': 0.15,  # amplitude of the stimulation current in pA
-              # 'probabilities'
-              }
+    params = {
+        "n_jobs": 12,  # number of threads used for parallelization in NEST
+        "N_E": 4000,  # number of excitatory neurons
+        "N_I": 1000,  # number of inhibitory neurons
+        "E_L": 0.0,  # leak potential
+        "V_th_E": 20.0,  # threshold potential for excitatory neurons
+        "V_th_I": 20.0,  # threshold potential for inhibitory neurons
+        "V_r": 0.0,  # reset potential
+        "C_m": 1.0,  # membrane capacity
+        "tau_E": 20.0,  # membrane time constant for excitatory neurons
+        "tau_I": 10.0,  # membrane time constant for inhibitory neurons
+        "t_ref": 5.0,  # refractory period
+        "ps": np.array([[0.2, 0.5], [0.5, 0.5]]),  # connection probabilities
+        "ge": 1.2,  # excitatory synaptic weight factor
+        "I_th_E": 1.25,  # Background current for excitatory neurons in multiples of I_rheobase
+        "I_th_I": 0.78,  # Background current for inhibitory neurons in multiples of I_rheobase
+        "dt": 0.1,  # simulation time step
+        "neuron_type": "iaf_psc_exp",  # neuron model used in NEST iaf and gif are available
+        "simtime": 10000,  # simulation time in ms
+        "warmup": 1000,  # warmup time in ms (time before recording starts)
+        "Q": 20,  # number of clusters
+        "clustering": "weight",  # type of clustering used 'weight' or
+        "stim_clusters": [2, 3, 4],  # list of clusters which are stimulated
+        "stim_starts": [2000, 6000],  # list of stimulation start times
+        "stim_ends": [3500, 7500],  # list of stimulation end times
+        "stim_amp": 0.15,  # amplitude of the stimulation current in pA
+        # 'probabilities'
+    }
 
     Rj = 0.82  # ration excitatory to inhibitory clustering
-    Rep = 6.  # excitatory clustering strength
+    Rep = 6.0  # excitatory clustering strength
 
     # Creates object which creates the EI clustered network in NEST
-    EI_Network = network.ClusteredNetwork.Clustered_Network_from_Rj_Rep(Rj, Rep, default, params)
+    EI_Network = network.ClusteredNetwork.Clustered_Network_from_Rj_Rep(
+        Rj, Rep, default, params
+    )
 
     # Runs the simulation and returns the spiketimes
     # get simulation initializes the network in NEST and runs the simulation
     # it returns a dict with the average rates, the spiketimes and the used parameters
     Result = EI_Network.get_simulation()
-    ax = general_helper.raster_plot(Result['spiketimes'], tlim=(0, params['simtime']), colorgroups=[
-        ('k', 0, params['N_E']), ("darkred", params['N_E'], params['N_E'] + params['N_I'])])
-    plt.savefig('Rasterplot.png')
-    print('Firing rate of excitatory neurons: ' + str(Result['e_rate']) + ' Spikes/s')
-    print('Firing rate of inhibitory neurons: ' + str(Result['i_rate']) + ' Spikes/s')
+    ax = general_helper.raster_plot(
+        Result["spiketimes"],
+        tlim=(0, params["simtime"]),
+        colorgroups=[
+            ("k", 0, params["N_E"]),
+            ("darkred", params["N_E"], params["N_E"] + params["N_I"]),
+        ],
+    )
+    plt.savefig("Rasterplot.png")
+    print("Firing rate of excitatory neurons: " + str(Result["e_rate"]) + " Spikes/s")
+    print("Firing rate of inhibitory neurons: " + str(Result["i_rate"]) + " Spikes/s")

From 4da274583786d39b599878586a44cd4cead0af4b Mon Sep 17 00:00:00 2001
From: Jessica Mitchell <j.mitchell@fz-juelich.de>
Date: Wed, 24 Jan 2024 10:38:00 +0100
Subject: [PATCH 06/23] shrink image and move to static folder, add EI to
 example index, add links to README

---
 doc/htmldoc/examples/index.rst                |  44 +++++++++++-------
 .../pynest/EI_clustered_network_schematic.png | Bin 0 -> 106060 bytes
 .../Network_Schematic.png                     | Bin 250599 -> 0 bytes
 .../examples/EI_clustered_network/README.rst  |  15 +++---
 4 files changed, 33 insertions(+), 26 deletions(-)
 create mode 100644 doc/htmldoc/static/img/pynest/EI_clustered_network_schematic.png
 delete mode 100644 pynest/examples/EI_clustered_network/Network_Schematic.png

diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst
index b7977d0076..7b8a0fb6aa 100644
--- a/doc/htmldoc/examples/index.rst
+++ b/doc/htmldoc/examples/index.rst
@@ -80,6 +80,17 @@ PyNEST examples
 
            * :doc:`../auto_examples/Potjans_2014/index`
 
+
+
+    .. grid-item-card:: EI clustered network (Rostami)
+           :img-top: ../static/img/pynest/EI_clustered_network_schematic.png
+
+           :doc:`../auto_examples/EI_clustered_network/index`
+
+
+
+.. grid:: 1 1 2 3
+
     .. grid-item-card:: GLIF (from Allen institute)
            :img-top: ../static/img/pynest/glif_cond.png
 
@@ -87,10 +98,6 @@ PyNEST examples
            * :doc:`../auto_examples/glif_psc_neuron`
            * :doc:`../auto_examples/glif_psc_double_alpha_neuron`
 
-
-
-.. grid:: 1 1 2 3
-
     .. grid-item-card:: Compartmental neurons
            :img-top: ../static/img/pynest/dendritic_synapse_conductances.png
 
@@ -98,13 +105,6 @@ PyNEST examples
            * :doc:`../auto_examples/compartmental_model/two_comps`
 
 
-    .. grid-item-card:: Rate neurons
-           :img-top: ../static/img/pynest/rate_neuron.png
-
-           * :doc:`../auto_examples/lin_rate_ipn_network`
-           * :doc:`../auto_examples/rate_neuron_dm`
-
-
 
     .. grid-item-card:: GIF (from Gerstner lab)
            :img-top: ../static/img/pynest/gif_pop.png
@@ -116,6 +116,13 @@ PyNEST examples
 
 .. grid:: 1 1 2 3
 
+    .. grid-item-card:: Rate neurons
+           :img-top: ../static/img/pynest/rate_neuron.png
+
+           * :doc:`../auto_examples/lin_rate_ipn_network`
+           * :doc:`../auto_examples/rate_neuron_dm`
+
+
     .. grid-item-card:: Hodgkin-Huxley
            :img-top: ../static/img/pynest/hh_phase.png
 
@@ -127,14 +134,14 @@ PyNEST examples
 
            * :doc:`../auto_examples/BrodyHopfield`
 
+.. grid:: 1 1 2 3
+
     .. grid-item-card:: Brette and Gerstner
            :img-top: ../static/img/pynest/brette_gerstner2c.png
 
            * :doc:`../auto_examples/brette_gerstner_fig_2c`
            * :doc:`../auto_examples/brette_gerstner_fig_3d`
 
-.. grid:: 1 1 2 3
-
 
     .. grid-item-card:: Precise spiking
            :img-top: ../static/img/pynest/precisespiking.png
@@ -146,11 +153,6 @@ PyNEST examples
 
            * :doc:`../auto_examples/CampbellSiegert`
 
-    .. grid-item-card:: SONATA network
-           :img-top: ../static/img/300_pointneurons.png
-
-           * :doc:`../auto_examples/sonata_example/sonata_network`
-
 
 .. grid:: 1 1 2 3
 
@@ -232,6 +234,11 @@ PyNEST examples
 
 .. grid:: 1 1 2 3
 
+    .. grid-item-card:: SONATA network
+           :img-top: ../static/img/300_pointneurons.png
+
+           * :doc:`../auto_examples/sonata_example/sonata_network`
+
     .. grid-item-card:: HPC benchmark
            :img-top: ../static/img/nest_logo-faded.png
 
@@ -331,6 +338,7 @@ PyNEST examples
    ../auto_examples/astrocytes/astrocyte_interaction
    ../auto_examples/astrocytes/astrocyte_small_network
    ../auto_examples/astrocytes/astrocyte_brunel
+   ../auto_examples/EI_clustered_network/index
 
 .. toctree::
    :hidden:
diff --git a/doc/htmldoc/static/img/pynest/EI_clustered_network_schematic.png b/doc/htmldoc/static/img/pynest/EI_clustered_network_schematic.png
new file mode 100644
index 0000000000000000000000000000000000000000..26e0fe5527c0c3b939099e33e3a947704b400297
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diff --git a/pynest/examples/EI_clustered_network/Network_Schematic.png b/pynest/examples/EI_clustered_network/Network_Schematic.png
deleted file mode 100644
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diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index 1b14d49c32..f5dd752b00 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -3,8 +3,7 @@ EI-clustered circuit model
 
 This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1]_.
 
-.. figure:: Network_Schematic.png
-   :scale: 50 %
+.. figure:: /static/img/pynest/EI_clustered_network_schematic.png
    :alt: EI-clustered circuit model
 
    Schematic of the EI-clustered circuit model. The network consists of Q clusters with one excitatory and one inhibitory population each.
@@ -16,11 +15,12 @@ If you use this code, we ask you to cite the paper by Rostami et al. [1]_ and th
 
 File structure
 --------------
-* `run_simulation.py`: an example script to try out the EI-clustered circuit model
-* `network.py`: the main Network class with functions to build and simulate the network
-* `helper.py`: helper functions for calculation of synaptic weights and currents
-* `general_helper.py`: helper functions to merge dictionaries and plot function for raster plots
-* `default_parameters.py`: default parameters for the network and simulation
+
+* :doc:`run_simulation.py <run_simulation>`: an example script to try out the EI-clustered circuit model
+* :doc:`network.py <network>`: the main Network class with functions to build and simulate the network
+* :doc:`helper.py <helper>`: helper functions for calculation of synaptic weights and currents
+* :doc:`general_helper.py <general_helper>`: helper functions to merge dictionaries and plot function for raster plots
+* :doc:`default_parameters.py <default_parameters>`: default parameters for the network and simulation
 
 Running the simulation
 ----------------------
@@ -64,4 +64,3 @@ References
 .. [2]  Schmitt FJ, Rostami V and Nawrot MP. 2023.
         Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN
         and NEST. Front. Neuroinform. 17:941696. DOI: `10.3389/fninf.2023.941696 <https://doi.org/10.3389/fninf.2023.941696>`__.
-

From 1e7671fbf7292b5ca6b9964fbb080a14acb8fe5d Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Mon, 29 Jan 2024 15:29:51 +0100
Subject: [PATCH 07/23] Adjust underline length to match title

---
 pynest/examples/EI_clustered_network/default_parameters.py | 2 +-
 pynest/examples/EI_clustered_network/general_helper.py     | 2 +-
 pynest/examples/EI_clustered_network/helper.py             | 2 +-
 pynest/examples/EI_clustered_network/network.py            | 2 +-
 pynest/examples/EI_clustered_network/run_simulation.py     | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index 9a9affa487..fae7085ac5 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: default parameters for EI-clustered network
--------------------------------------------
+----------------------------------------------------------------------------
 
 default parameters for EI-clustered network simulation
 """
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index 4215c04756..06e86522d3 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: general helper functions
--------------------------------------------
+--------------------------------------------------------
 
 Helper functions for merging dictionaries and some plotting functions.
 """
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index 2d7cbcc46b..b7877e7c4e 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: Helper Functions
--------------------------------------------
+------------------------------------------------
 
 Helper functions to calculate synaptic weights and first passage times for exponential synapses to construct
 random balanced networks.
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 8ea0adbb9d..287b223c29 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: Network Class
-----------------------------------------
+----------------------------------------------
 
 Main file of the EI-clustered network defining the ``ClusteredNetwork`` class with functions to
 build and simulate the network.
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 1c8479e2c0..9cc1519704 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: Run Simulation
-----------------------------------------
+-----------------------------------------------
 
 This is an example script for running the EI-clustered model with two stimulations
 and generating a raster plot.

From 3a3f444f56179316ba17e892f2936333293ea5df Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Tue, 30 Jan 2024 14:48:11 +0100
Subject: [PATCH 08/23] Change docstring style to numpy docstring

---
 .../default_parameters.py                     |   2 +-
 .../EI_clustered_network/general_helper.py    |  83 ++++++--
 .../examples/EI_clustered_network/helper.py   | 102 ++++++---
 .../examples/EI_clustered_network/network.py  | 198 +++++++++++-------
 4 files changed, 261 insertions(+), 124 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index fae7085ac5..5901aa50d5 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -20,7 +20,7 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: default parameters for EI-clustered network
-----------------------------------------------------------------------------
+---------------------------------------------------------------------------
 
 default parameters for EI-clustered network simulation
 """
diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
index 06e86522d3..151bf36064 100644
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ b/pynest/examples/EI_clustered_network/general_helper.py
@@ -32,21 +32,40 @@
 
 
 def merge_params(params, default_values):
-    """
+    """Merge default values with params dictionary.
+
     Updates default Values defined in a module with params-dictionary.
-    :param params: dictionary of parameters
-    :param default_values: module with default values
-    :return: updated dictionary
+
+    Parameters
+    ----------
+    params: dict
+         Override parameters.
+    default_values: module
+        Parameters with default values.
+
+    Returns
+    -------
+    dict
+        Dictionary with default values updated by params.
     """
     return nested_update(module_to_dict(default_values), params)
 
 
 def module_to_dict(module):
-    """
+    """Create dict of module variables.
+
     Creates dict of explicit variables in module which are not imported modules.
     Generates for each variable name an entry in the dictionary.
-    :param module: module
-    :return: dict of explicit variables
+
+    Parameters
+    ----------
+    module: module
+        Module to be converted to dict.
+
+    Returns
+    -------
+    dict
+        Dictionary with module variables.
     """
     module_dict = {}
     if module:
@@ -63,11 +82,21 @@ def module_to_dict(module):
 
 
 def nested_update(d, d2):
-    """
-    Updates values in d with values of d2. Adds new keys if they are not present.
-    :param d: dictionary to be updated
-    :param d2: dictionary with update values
-    :return: updated dictionary
+    """Update nested dictionary.
+
+    Update values in one dictionary with values of second dictionary. Adds new keys if they are not present.
+
+    Parameters
+    ----------
+    d: dict
+        Dictionary to be updated.
+    d2: dict
+        Dictionary with update values.
+
+    Returns
+    -------
+    dict
+        Updated dictionary.
     """
     d_local = copy.deepcopy(d)
     for key in d2:
@@ -79,14 +108,28 @@ def nested_update(d, d2):
 
 
 def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
-    """
-    Plots raster plot of spiketimes.
-    :param spiketimes: spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
-    :param tlim: time limits of plot (list): [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
-    :param colorgroups: list of tuples (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons, if None: black
-    :param ax: axis object, if None: new figure
-    :param markersize: size of markers
-    :return: axis object
+    """Raster plot of spiketimes.
+
+    Plots raster plot of spiketimes withing given time limits and colors neurons according to colorgroups.
+
+    Parameters
+    ----------
+    spiketimes: ndarray
+        2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+    tlim: list of floats (optional)
+        Time limits of plot: [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
+    colorgroups: list of tuples (optional)
+        Each element is a tuple in the format (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons in
+        given range, if None is given all neurons are black.
+    ax: axis object (optional)
+        If None a new figure is created, else the plot is added to the given axis.
+    markersize: float (optional)
+        Size of markers. (default: 0.5)
+
+    Returns
+    -------
+    axis object
+        Axis object with raster plot.
     """
     if ax is None:
         fig, ax = plt.subplots()
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index b7877e7c4e..184567eaa8 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -32,13 +32,23 @@
 
 
 def max_psp_exp(tau_m, tau_syn, c_m=1.0, e_l=0.0):
-    """
-    Calculates the maximum psp amplitude for exponential synapses and unit J.
-    :param tau_m: Membrane time constant [ms]
-    :param tau_syn: Synapse time constant  [ms]
-    :param c_m: Membrane capacity [pF]
-    :param e_l: Resting potential [mV]
-    :return: maximum psp amplitude
+    """Maximum psp amplitude for exponential synapses and unit J.
+
+    Parameters
+    ----------
+    tau_m: float
+        Membrane time constant [ms]
+    tau_syn: float
+        Synapse time constant  [ms]
+    c_m: float (optional)
+        Membrane capacity [pF] (default: 1.0)
+    e_l: float (optional)
+        Resting potential [mV] (default: 0.0)
+
+    Returns
+    -------
+    float
+        maximum psp amplitude [mV]
     """
     tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)
     B = tau_m * tau_syn / c_m / (tau_syn - tau_m)
@@ -46,10 +56,17 @@ def max_psp_exp(tau_m, tau_syn, c_m=1.0, e_l=0.0):
 
 
 def calc_js(params):
-    """
-    calculates the synaptic weights for exponential synapses before clustering
-    :param params: dictionary of network parameters
-    :return: synaptic weights 2x2 matrix
+    """Calculate synaptic weights for exponential synapses before clustering.
+
+    Parameters
+    ----------
+    params: dict
+        Dictionary of network parameters
+
+    Returns
+    -------
+    ndarray
+        synaptic weights 2x2 matrix [[EE, EI], [IE, II]]
     """
     N_E = params.get("N_E")  # excitatory units
     N_I = params.get("N_I")  # inhibitory units
@@ -88,15 +105,27 @@ def calc_js(params):
 
 
 def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
-    """
-    calculate first pasage time between Vstart and Vtarget.
-    :param tau_m: Membrane time constant [ms]
-    :param e_l: Resting potential [mV]
-    :param i_e: external current [pA]
-    :param c_m: Membrane capacity [pF]
-    :param vtarget: target voltage [mV]
-    :param vstart: start voltage [mV]
-    :return: first passage time [ms]
+    """Calculate first pasage time between Vstart and Vtarget.
+
+    Parameters
+    ----------
+    tau_m: float
+        Membrane time constant [ms]
+    e_l: float
+        Resting potential [mV]
+    i_e: float
+        external current [pA]
+    c_m: float
+        Membrane capacity [pF]
+    vtarget: float
+        target voltage [mV]
+    vstart: float
+        start voltage [mV]
+
+    Returns
+    -------
+    float
+        first passage time [ms]
     """
     inner = (vtarget - e_l - tau_m * i_e / c_m) / (
         vstart - e_l - tau_m * i_e / c_m + small
@@ -108,16 +137,29 @@ def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
 
 
 def v_fpt(tau_m, e_l, i_e, c_m, ttarget, vtarget, t_ref):
-    """
-    Calculate the initial voltage required to obtain a certain first passage time.
-    :param tau_m: Membrane time constant [ms]
-    :param e_l: Resting potential [mV]
-    :param i_e: external current [pA]
-    :param c_m: Membrane capacity [pF]
-    :param ttarget: target first passage time [ms]
-    :param vtarget: target voltage [mV]
-    :param t_ref: refractory period [ms]
-    :return: initial voltage [mV]
+    """Initial voltage to obtain a certain first passage time.
+
+    Parameters
+    ----------
+    tau_m: float
+        Membrane time constant [ms]
+    e_l: float
+        Resting potential [mV]
+    i_e: float
+        external current [pA]
+    c_m: float
+        Membrane capacity [pF]
+    ttarget: float
+        target first passage time [ms]
+    vtarget: float
+        target voltage [mV]
+    t_ref: float
+        refractory period [ms]
+
+    Returns
+    -------
+    float
+        initial voltage [mV]
     """
     return (
         (vtarget - e_l - tau_m * i_e / c_m) * np.exp(ttarget / tau_m)
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 287b223c29..7e96eea08a 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -20,11 +20,10 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 """PyNEST EI-clustered network: Network Class
-----------------------------------------------
+---------------------------------------------
 
 Main file of the EI-clustered network defining the ``ClusteredNetwork`` class with functions to
 build and simulate the network.
-
 """
 
 import pickle
@@ -37,21 +36,35 @@
 
 
 class ClusteredNetwork(object):
-    """Provides functions to create neuron populations, stimulation devices and recording devices for an
-    EI-clustered network and setups the simulation in NEST (v3.x).
+    """EI-clustered network objeect to build and simulate the network.
 
-    Creates an object with functions to create neuron populations,
-    stimulation devices and recording devices for an EI-clustered network.
-    Initializes the object. Creates the attributes Populations, RecordingDevices and
-    Currentsources to be filled during network construction.
-    Attribute params contains all parameters used to construct network.
+    Provides functions to create neuron populations, stimulation devices and recording devices for an
+    EI-clustered network and setups the simulation in NEST (v3.x).
 
-    Parameters:
-        defaultValues (module): A Module which contains the default configuration
-        parameters (dict):      Dictionary with parameters which should be modified from their default values
+    Attributes
+    ----------
+    params: dict
+        Dictionary with parameters used to construct network.
+    Populations: list
+        List of neuron population groups.
+    RecordingDevices: list
+        List of recording devices.
+    Currentsources: list
+        List of current sources.
+    ModelBuildPipeline: list
+        List of functions to build the network.
     """
 
     def __init__(self, defaultValues, parameters):
+        """Initialize the ClusteredNetwork object.
+
+        Parameters
+        ----------
+        defaultValues : module
+            A Module which contains the default configuration
+        parameters : dict
+            Dictionary with parameters which should be modified from their default values
+        """
         self.params = general_helper.merge_params(parameters, defaultValues)
         self.Populations = []
         self.RecordingDevices = []
@@ -66,18 +79,30 @@ def __init__(self, defaultValues, parameters):
 
     @classmethod
     def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
-        """Creates a ClusteredNetwork object with parameters Rj and Rep
+        """Creates a ClusteredNetwork object from parameters Rj and Rep
 
         Rj and Rep are used to calculate the parameters jplus (weight clustering) or pplus (probability clustering)
 
-        Parameters:
-            Rj (float):           Ratio excitatory to inhibitory clustering strength
-            Rep (float):          Excitatory clustering strength
-            defaultValues (module): A Module which contains the default configuration
-            parameters (dict):      Dictionary with parameters which should be modified from their default values
-
-        Returns:
-            object: ClusteredNetwork object with parameters
+        Parameters
+        ----------
+        Rj : float
+            Ratio excitatory to inhibitory clustering strength
+        Rep : float
+            Excitatory clustering strength
+        defaultValues : module
+            A Module which contains the default configuration
+        parameters : dict
+            Dictionary with parameters which should be modified from their default values
+
+        Returns
+        -------
+        ClusteredNetwork
+            ClusteredNetwork object
+
+        Raises
+        ----------
+        ValueError
+            If the clustering type is not recognized
         """
         params = general_helper.merge_params(parameters, defaultValues)
         if params["clustering"] == "weight":
@@ -93,10 +118,7 @@ def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
         return cls(defaultValues, params)
 
     def clean_network(self):
-        """Creates clean network
-
-        Creates empty attributes of a network.
-        """
+        """Clears the object of all attributes."""
         self.Populations = []
         self.RecordingDevices = []
         self.Currentsources = []
@@ -121,7 +143,6 @@ def create_populations(self):
         """Create all neuron populations.
 
         Q excitatory and inhibitory neuron populations with the parameters of the network are created.
-
         """
         # make sure number of clusters and units are compatible
         assert (
@@ -317,8 +338,12 @@ def create_populations(self):
         self.Populations = [E_pops, I_pops]
 
     def connect(self):
-        """
-        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        """Connect the excitatory and inhibitory populations with each other in the EI-clustered scheme
+
+        Raises
+        ------
+        ValueError
+            If the clustering method is not recognized
         """
         # if CLustering is set to "Weight" or is not set at all or is not in dictionary, use the connect_weight function
         try:
@@ -334,7 +359,9 @@ def connect(self):
             self.connect_weight()
 
     def connect_probabilities(self):
-        """Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        """Connect the clusters with a probability EI-clustered scheme
+
+        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
         by increasing the probabilies of the connections within the clusters and decreasing the probabilites of the
         connections between the clusters. The weights are calculated so that the total input to a neuron
         is balanced.
@@ -559,7 +586,9 @@ def connect_probabilities(self):
                     nest.Connect(pre, post, conn_params_II_minus, "II")
 
     def connect_weight(self):
-        """Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        """Connect the clusters with a weight EI-clustered scheme
+
+        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
         by increasing the weights of the connections within the clusters and decreasing the weights of the
         connections between the clusters. The weights are calculated so that the total input to a neuron
         is balanced.
@@ -744,9 +773,7 @@ def connect_weight(self):
         # print('Js: ', js / np.sqrt(N))
 
     def create_stimulation(self):
-        """
-        Creates a current source and connects it to the specified cluster/s.
-        """
+        """Create a current source and connect it to clusters."""
         if self.params["stim_clusters"] is not None:
             stim_amp = self.params[
                 "stim_amp"
@@ -772,8 +799,9 @@ def create_stimulation(self):
             )
 
     def create_recording_devices(self):
-        """
-        Creates a spike recorder connected to all neuron populations created by create_populations
+        """Creates a spike recorder
+
+        Create and connect a spike recorder to all neuron populations in self.Populations.
         """
         self.RecordingDevices = [nest.Create("spike_recorder")]
         self.RecordingDevices[0].record_to = "memory"
@@ -786,15 +814,18 @@ def create_recording_devices(self):
         nest.Connect(all_units, self.RecordingDevices[0], "all_to_all")  # Spikerecorder
 
     def set_model_build_pipeline(self, Pipeline):
-        """
-        Sets the ModelBuildPipeline.
-        Parameters:
-            Pipeline (list of functions):   ordered list of functions executed to build the network model
+        """Set ModelBuildPipeline
+
+        Parameters
+        ----------
+        Pipeline: list
+            ordered list of functions executed to build the network model
         """
         self.ModelBuildPipeline = Pipeline
 
     def setup_network(self):
-        """
+        """Setup network in NEST
+
         Initializes NEST and creates the network in NEST, ready to be simulated.
         Functions saved in ModelBuildPipeline are executed.
         nest.Prepare is executed in this function.
@@ -804,22 +835,23 @@ def setup_network(self):
         nest.Prepare()
 
     def simulate(self):
-        """
-        Simulates network for a period of warmup+simtime
-        """
+        """Simulates network for a period of warmup+simtime"""
         if self.params["warmup"] + self.params["simtime"] <= 0.1:
             pass
         else:
             nest.Run(self.params["warmup"] + self.params["simtime"])
 
     def get_recordings(self):
-        """
-        Extracts spikes form the Spikerecorder connected to all populations created in create_populations.
+        """Extract spikes from Spikerecorder
+
+        Extract spikes form the Spikerecorder connected to all populations created in create_populations.
         Cuts the warmup period away and sets time relative to end of warmup.
         Ids 1:N_E correspond to excitatory neurons, N_E+1:N_E+N_I correspond to inhibitory neurons.
 
-        Returns:
-            spiketimes (np.array): Row 0: spiketimes, Row 1: neuron ID.
+        Returns
+        -------
+        spiketimes: ndarray
+            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
         """
         events = nest.GetStatus(self.RecordingDevices[0], "events")[0]
         # convert them to the format accepted by spiketools
@@ -833,34 +865,40 @@ def get_recordings(self):
         return spiketimes
 
     def get_parameter(self):
-        """
-        Returns all parameters used to create the network.
-        Return:
-            parameters (dict): Dictionary with all parameters for the simulation / network creation.
+        """Get all parameters used to create the network.
+        Returns
+        -------
+        dict
+            Dictionary with all parameters of the network and the simulation.
         """
         return self.params
 
     def create_and_simulate(self):
-        """
-        Creates the EI-clustered network and simulates it with the parameters supplied in the object creation.
+        """Create and simulate the EI-clustered network.
 
-        Returns:
-            spiketimes (np.array):  Row 0: spiketimes, Row 1: neuron ID.
-                                    Ids 1:N_E correspond to excitatory neurons,
-                                    N_E+1:N_E+N_I correspond to inhibitory neurons.
+        Returns
+        -------
+        spiketimes: ndarray
+            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
         """
         self.setup_network()
         self.simulate()
         return self.get_recordings()
 
     def get_firing_rates(self, spiketimes=None):
-        """
+        """Calculates the firing rates of all excitatory neurons and the firing rates of all inhibitory neurons.
+
         Calculates the firing rates of all excitatory neurons and the firing rates of all inhibitory neurons
         created by self.create_populations. If spiketimes are not supplied, they get extracted.
-        Parameters:
-            spiketimes: (optional, np.array 2xT)   spiketimes of simulation
-        Returns:
-            (e_rate, i_rate) average firing rate of excitatory/inhibitory neurons (spikes/s)
+        Parameters
+        ----------
+        spiketimes: ndarray
+            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+
+        Returns
+        -------
+        tuple[float, float]
+            average firing rates of excitatory (0) and inhibitory (1) neurons (spikes/s)
         """
         if spiketimes is None:
             spiketimes = self.get_recordings()
@@ -875,9 +913,16 @@ def get_firing_rates(self, spiketimes=None):
         return e_rate, i_rate
 
     def set_I_x(self, I_XE, I_XI):
-        """
-        Adds DC currents for the excitatory and inhibitory neurons. Allows also the usage of a baseline defined by the
-        model setup.
+        """Set DC currents for excitatory and inhibitory neurons
+        Adds DC currents for the excitatory and inhibitory neurons. The DC currents are added to the currents already
+        present in the populations.
+
+        Parameters
+        ----------
+        I_XE: float
+            extra DC current for excitatory neurons [pA]
+        I_XI: float
+            extra DC current for inhibitory neurons [pA]
         """
         for E_pop in self.Populations[0]:
             I_e_loc = E_pop.get("I_e")
@@ -887,14 +932,21 @@ def set_I_x(self, I_XE, I_XI):
             I_pop.set({"I_e": I_e_loc + I_XI})
 
     def get_simulation(self, PathSpikes=None):
-        """
-        Creates the network, simulates it and extracts the firing rates. If PathSpikes is supplied the spikes get saved
-        to a pickle file. If a timeout is supplied, a timeout handler is created which stops the execution.
-        Parameters:
-            PathSpikes: (optional) Path of file for spiketimes
-            timeout: (optional) Time of timeout in seconds
-        Returns:
-            Dictionary with firing rates, timing information (dict) and parameters (dict)
+        """Create network, simulate and return results
+
+        Creates the EI-clustered network and simulates it with the parameters supplied in the object creation.
+        Returns a dictionary with firing rates, timing information (dict) and parameters (dict). If PathSpikes is
+        supplied the spikes get saved to a pickle file.
+
+        Parameters
+        ----------
+        PathSpikes: str (optional)
+            Path of file for spiketimes, if None, no file is saved
+
+        Returns
+        -------
+        dict
+         Dictionary with firing rates, spiketimes (ndarray) and parameters (dict)
         """
 
         self.setup_network()

From 3fb03d1b7900f0a259eccca61752bfe4efa1cbd4 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Wed, 31 Jan 2024 13:53:31 +0100
Subject: [PATCH 09/23] Fixed Acknowledgments

---
 pynest/examples/EI_clustered_network/README.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index f5dd752b00..a6d6d81439 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -4,7 +4,7 @@ EI-clustered circuit model
 This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1]_.
 
 .. figure:: /static/img/pynest/EI_clustered_network_schematic.png
-   :alt: EI-clustered circuit model
+   :alt: EI-clustered circuit model.
 
    Schematic of the EI-clustered circuit model. The network consists of Q clusters with one excitatory and one inhibitory population each.
 
@@ -47,6 +47,7 @@ in parts through the Collaborative Research Center ’Motor Control in Health an
 German Excellence Initiative (DFG-ZUK 81/1) and in parts through the DFG graduate school
 ’Neural Circuit Analysis’ (DFG-RTG 1960, ID 365082554) and through the European Union’s Horizon 2020 Framework
 Programme for Research and Innovation under grant agreement number 945539 (Human Brain Project SGA3).
+The figure is created with BioRender.com.
 
 Other implementations of the EI-clustered model
 -----------------------------------------------

From 21e1f77420ba56c9ee3487155c0e590ea4e43b18 Mon Sep 17 00:00:00 2001
From: Felix Schmitt <felix.schmitt@pegasus-elektra.de>
Date: Thu, 7 Mar 2024 13:19:49 +0100
Subject: [PATCH 10/23] Apply suggestions from code review

Co-authored-by: Hans Ekkehard Plesser <hans.ekkehard.plesser@nmbu.no>
---
 pynest/examples/EI_clustered_network/README.rst            | 4 ++--
 pynest/examples/EI_clustered_network/default_parameters.py | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index a6d6d81439..9368b868c4 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -1,5 +1,5 @@
 EI-clustered circuit model
-===========================
+==========================
 
 This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1]_.
 
@@ -27,7 +27,7 @@ Running the simulation
 
 .. code-block:: bash
 
-   python3 run_simulation.py
+   python run_simulation.py
 
 A raster plot of the network activity is saved as ``Rasterplot.png``.
 
diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
index 5901aa50d5..28640b9352 100644
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ b/pynest/examples/EI_clustered_network/default_parameters.py
@@ -24,6 +24,7 @@
 
 default parameters for EI-clustered network simulation
 """
+
 import numpy as np
 
 ############################################

From d6c35df5a72b6fd8ca022921ae464ef760e0d4c5 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 10:52:08 +0100
Subject: [PATCH 11/23] Adapt structure of parameters from Potjans 2014 example

---
 .../default_parameters.py                     | 111 ------------------
 .../EI_clustered_network/network_params.py    |  97 +++++++++++++++
 .../EI_clustered_network/sim_params.py        |  41 +++++++
 .../EI_clustered_network/stimulus_params.py   |  38 ++++++
 4 files changed, 176 insertions(+), 111 deletions(-)
 delete mode 100644 pynest/examples/EI_clustered_network/default_parameters.py
 create mode 100644 pynest/examples/EI_clustered_network/network_params.py
 create mode 100644 pynest/examples/EI_clustered_network/sim_params.py
 create mode 100644 pynest/examples/EI_clustered_network/stimulus_params.py

diff --git a/pynest/examples/EI_clustered_network/default_parameters.py b/pynest/examples/EI_clustered_network/default_parameters.py
deleted file mode 100644
index 28640b9352..0000000000
--- a/pynest/examples/EI_clustered_network/default_parameters.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# default_parameters.py
-#
-# This file is part of NEST.
-#
-# Copyright (C) 2004 The NEST Initiative
-#
-# NEST is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-#
-# NEST is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
-
-"""PyNEST EI-clustered network: default parameters for EI-clustered network
----------------------------------------------------------------------------
-
-default parameters for EI-clustered network simulation
-"""
-
-import numpy as np
-
-############################################
-# general parameters
-############################################
-eps = np.finfo(float).eps
-n_jobs = 1
-
-dt = 0.1
-simtime = 1000.0
-warmup = 0.0
-record_voltage = False
-record_from = "all"
-recording_interval = dt
-return_weights = False
-
-############################################
-# neuron parameters
-############################################
-neuron_type = "iaf_psc_exp"  # 'gif_psc_exp'
-E_L = 0.0
-C_m = 1.0
-tau_E = 20.0
-tau_I = 10.0
-t_ref = 5.0
-V_th_E = 20.0
-V_th_I = 20.0
-V_r = 0.0
-I_xE = 1.0
-I_xI = 2.0
-delta_I_xE = 0.0
-delta_I_xI = 0.0
-I_th_E = 1.25
-I_th_I = 0.78
-V_m = "rand"
-
-# synapse parameters
-tau_syn_ex = 3.0
-tau_syn_in = 2.0
-delay = 0.1  # synaptic delay
-
-# Distribution of synaptic weights
-# available distributions= https://nest-simulator.readthedocs.io/en/stable/guides/connection_management.html#dist-params
-
-DistParams = {"distribution": "normal", "sigma": 0.0, "fraction": False}
-
-syn_params = {"U": 0.2, "u": 0.0, "tau_rec": 120.0, "tau_fac": 0.0}
-
-############################################
-# network parameters
-############################################
-
-# number of units
-N_E = 1200
-N_I = 300
-
-# cluster number
-Q = 6
-# cluster weight ratios
-jplus = np.ones((2, 2))
-
-# connection probabilities
-ps = np.array([[0.2, 0.5], [0.5, 0.5]])
-
-# connections strengths
-# weights are js/sqrt(N)
-# nan means they are calculated
-js = np.ones((2, 2)) * np.nan
-# factors for inhibitory weights
-ge = 1.2
-gi = 1.0
-gie = 1.0
-
-# factor multiplied with weights
-s = 1.0
-fixed_indegree = False
-
-############################################
-# stimulation parameters
-############################################
-stim_clusters = None  # clusters to be stimulated
-stim_amp = 0.0  # amplitude of the stimulation current in pA
-stim_starts = []  # list of stimulation start times
-stim_ends = []  # list of stimulation end times
diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
new file mode 100644
index 0000000000..7122e13b51
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+#
+# sim_params.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: Network Parameters
+------------------------------------------------
+
+A dictionary with parameters defining the network and neuron parameters.
+
+"""
+
+import numpy as np
+
+net_dict = {
+    ############################################
+    # neuron parameters
+    ############################################
+    # neuron model
+    "neuron_type": "iaf_psc_exp",
+    # Resting potential [mV]
+    "E_L": 0.0,
+    # Membrane capacitance [pF]
+    "C_m": 1.0,
+    # Membrane time constant for excitatory neurons [ms]
+    "tau_E": 20.0,
+    # Membrane time constant for inhibitory neurons [ms]
+    "tau_I": 10.0,
+    # Refractory period [ms]
+    "t_ref": 5.0,
+    # Threshold for excitatory neurons [mV]
+    "V_th_E": 20.0,
+    # Threshold for inhibitory neurons [mV]
+    "V_th_I": 20.0,
+    # Reset potential [mV]
+    "V_r": 0.0,
+    # synaptic time constant for excitatory synapses [ms]
+    "tau_syn_ex": 5.0,
+    # synaptic time constant for inhibitory synapses [ms]
+    "tau_syn_in": 5.0,
+    # synaptic delay [ms]
+    "delay": 0.1,
+    # Feed forward excitatory input [rheobase current]
+    "I_th_E": 1.25,
+    # Feed forward inhibitory input [rheobase current]
+    "I_th_I": 0.78,
+    # distribution of feed forward input, uniform around I_th with half width I_width
+    "delta_I_xE": 0.0,  # excitatory
+    "delta_I_xI": 0.0,  # inhibitory
+    # initial membrane potential
+    "V_m": "rand",
+    ############################################
+    # network parameters
+    ############################################
+    # number of excitatory neurons in the network
+    # Neurons per cluster N_E/n_clusters
+    "N_E": 4000,
+    # number of inhibitory neurons in the network
+    "N_I": 1000,
+    # Number of clusters
+    "n_clusters": 20,
+    # connection probabilities
+    # baseline_conn_prob[0, 0] E to E, baseline_conn_prob[0, 1] I to E,
+    # baseline_conn_prob[1, 0] E to I, baseline_conn_prob[1, 1] I to I
+    "baseline_conn_prob": np.array([[0.2, 0.5], [0.5, 0.5]]),
+    # inhibitory weight ratios - scaling like random balanced network
+    "gei": 1.2,  # I to E
+    "gie": 1.0,  # E to I
+    "gii": 1.0,  # I to I
+    # additional scaling factor for all weights - can be used to scale weights with network size
+    "s": 1.0,
+    # fixed indegree - if false the synapses are established with probability ps
+    "fixed_indegree": False,
+    # cluster network by weight or connection probability - "weight" or "probabilities"
+    "clustering": "weight",
+    # ratio excitatory to inhibitory clustering, rj = 0 means no clustering, which resembles a clustered network
+    # with a blanket of inhibition
+    "rj": 0.82,
+    # excitatory clustering factor, rep = 1 means no clustering, reselmbles a balanced random network
+    "rep": 6.0,
+}
diff --git a/pynest/examples/EI_clustered_network/sim_params.py b/pynest/examples/EI_clustered_network/sim_params.py
new file mode 100644
index 0000000000..30ac2cd330
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/sim_params.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+#
+# sim_params.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""PyNEST EI-clustered network: Simulation Parameters
+------------------------------------------------
+
+A dictionary with parameters defining the simulation.
+
+"""
+
+sim_dict = {
+    # The full simulation time is the sum of a presimulation time and the main
+    # simulation time.
+    # presimulation time (in ms)
+    "warmup": 1000.0,
+    # simulation time (in ms)
+    "simtime": 10000.0,
+    # resolution of the simulation (in ms)
+    "dt": 0.1,
+    "randseed": 55,
+    # Number of virtual processes
+    "n_vp": 4,
+}
diff --git a/pynest/examples/EI_clustered_network/stimulus_params.py b/pynest/examples/EI_clustered_network/stimulus_params.py
new file mode 100644
index 0000000000..a78f430be4
--- /dev/null
+++ b/pynest/examples/EI_clustered_network/stimulus_params.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+#
+# stimulus_params.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+""" PyNEST EI-clustered network: Stimulus Parameters
+-----------------------------------------------
+
+A dictionary with parameters for an optinal stimulation of clusters.
+
+"""
+
+stim_dict = {
+    # list of clusters to be stimulated (None for no stimulation, 0-n_clusters-1)
+    "stim_clusters": [2, 3, 4],
+    # stimulus amplitude (in pA)
+    "stim_amp": 0.15,
+    # stimulus start times in ms: list (warmup time is added automatically)
+    "stim_starts": [2000, 6000],
+    # list of stimulus end times in ms (warmup time is added automatically)
+    "stim_ends": [3500, 7500],
+}

From 0b1923d1d9bc8924047ae2ab59687340657e4410 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 10:55:30 +0100
Subject: [PATCH 12/23] Only one helper module with less functions needed

---
 .../EI_clustered_network/general_helper.py    | 153 ------------------
 .../examples/EI_clustered_network/helper.py   | 134 ++++++++-------
 2 files changed, 66 insertions(+), 221 deletions(-)
 delete mode 100644 pynest/examples/EI_clustered_network/general_helper.py

diff --git a/pynest/examples/EI_clustered_network/general_helper.py b/pynest/examples/EI_clustered_network/general_helper.py
deleted file mode 100644
index 151bf36064..0000000000
--- a/pynest/examples/EI_clustered_network/general_helper.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# general_helper.py
-#
-# This file is part of NEST.
-#
-# Copyright (C) 2004 The NEST Initiative
-#
-# NEST is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-#
-# NEST is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
-
-"""PyNEST EI-clustered network: general helper functions
---------------------------------------------------------
-
-Helper functions for merging dictionaries and some plotting functions.
-"""
-import copy
-from types import ModuleType
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def merge_params(params, default_values):
-    """Merge default values with params dictionary.
-
-    Updates default Values defined in a module with params-dictionary.
-
-    Parameters
-    ----------
-    params: dict
-         Override parameters.
-    default_values: module
-        Parameters with default values.
-
-    Returns
-    -------
-    dict
-        Dictionary with default values updated by params.
-    """
-    return nested_update(module_to_dict(default_values), params)
-
-
-def module_to_dict(module):
-    """Create dict of module variables.
-
-    Creates dict of explicit variables in module which are not imported modules.
-    Generates for each variable name an entry in the dictionary.
-
-    Parameters
-    ----------
-    module: module
-        Module to be converted to dict.
-
-    Returns
-    -------
-    dict
-        Dictionary with module variables.
-    """
-    module_dict = {}
-    if module:
-        module_dict = {
-            key: value
-            for key, value in module.__dict__.items()
-            if not (
-                key.startswith("__")
-                or key.startswith("_")
-                or isinstance(value, ModuleType)
-            )
-        }
-    return module_dict
-
-
-def nested_update(d, d2):
-    """Update nested dictionary.
-
-    Update values in one dictionary with values of second dictionary. Adds new keys if they are not present.
-
-    Parameters
-    ----------
-    d: dict
-        Dictionary to be updated.
-    d2: dict
-        Dictionary with update values.
-
-    Returns
-    -------
-    dict
-        Updated dictionary.
-    """
-    d_local = copy.deepcopy(d)
-    for key in d2:
-        if isinstance(d2[key], dict) and key in d_local:
-            d_local[key] = nested_update(d_local[key], d2[key])
-        else:
-            d_local[key] = d2[key]
-    return d_local
-
-
-def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
-    """Raster plot of spiketimes.
-
-    Plots raster plot of spiketimes withing given time limits and colors neurons according to colorgroups.
-
-    Parameters
-    ----------
-    spiketimes: ndarray
-        2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
-    tlim: list of floats (optional)
-        Time limits of plot: [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
-    colorgroups: list of tuples (optional)
-        Each element is a tuple in the format (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons in
-        given range, if None is given all neurons are black.
-    ax: axis object (optional)
-        If None a new figure is created, else the plot is added to the given axis.
-    markersize: float (optional)
-        Size of markers. (default: 0.5)
-
-    Returns
-    -------
-    axis object
-        Axis object with raster plot.
-    """
-    if ax is None:
-        fig, ax = plt.subplots()
-    if tlim is None:
-        tlim = [min(spiketimes[0]), max(spiketimes[0])]
-    if colorgroups is None:
-        colorgroups = [("k", 0, max(spiketimes[1]))]
-    for color, start, stop in colorgroups:
-        ax.plot(
-            spiketimes[0][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
-            spiketimes[1][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
-            color=color,
-            marker=".",
-            linestyle="None",
-            markersize=markersize,
-        )
-    ax.set_xlim(tlim)
-    ax.set_ylim([0, max(spiketimes[1])])
-    ax.set_xlabel("Time [ms]")
-    ax.set_ylabel("Neuron ID")
-    return ax
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index 184567eaa8..bf9e05e608 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -23,16 +23,17 @@
 ------------------------------------------------
 
 Helper functions to calculate synaptic weights and first passage times for exponential synapses to construct
-random balanced networks.
+random balanced networks and plot raster plot with color groups.
 """
 
+import matplotlib.pyplot as plt
 import numpy as np
 
 small = 1e-10
 
 
-def max_psp_exp(tau_m, tau_syn, c_m=1.0, e_l=0.0):
-    """Maximum psp amplitude for exponential synapses and unit J.
+def postsynaptic_current_to_potential(tau_m, tau_syn, c_m=1.0, e_l=0.0):
+    """Maximum post-synaptic potential amplitude for exponential synapses and a synaptic efficacy J of 1 pA.
 
     Parameters
     ----------
@@ -48,15 +49,15 @@ def max_psp_exp(tau_m, tau_syn, c_m=1.0, e_l=0.0):
     Returns
     -------
     float
-        maximum psp amplitude [mV]
+        maximum psp amplitude (for a 1 pA current spike) [mV]
     """
-    tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)
-    B = tau_m * tau_syn / c_m / (tau_syn - tau_m)
-    return (e_l - B) * np.exp(-tmax / tau_m) + B * np.exp(-tmax / tau_syn)
+    tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)  # time of maximum deflection of the psp  [ms]
+    pre = tau_m * tau_syn / c_m / (tau_syn - tau_m)  # we assume here the current spike is 1 pA, otherwise [mV/pA]
+    return (e_l - pre) * np.exp(-tmax / tau_m) + pre * np.exp(-tmax / tau_syn)
 
 
-def calc_js(params):
-    """Calculate synaptic weights for exponential synapses before clustering.
+def calculate_RBN_weights(params):
+    """Calculate synaptic weights for exponential synapses before clustering, for a random balanced network.
 
     Parameters
     ----------
@@ -71,41 +72,34 @@ def calc_js(params):
     N_E = params.get("N_E")  # excitatory units
     N_I = params.get("N_I")  # inhibitory units
     N = N_E + N_I  # total units
-    ps = params.get("ps")  # connection probs
-    ge = params.get("ge")
-    gi = params.get("gi")
+    baseline_conn_prob = params.get("baseline_conn_prob")  # connection probs
+    gei = params.get("gei")
+    gii = params.get("gii")
     gie = params.get("gie")
     V_th_E = params.get("V_th_E")  # threshold voltage
     V_th_I = params.get("V_th_I")
     tau_E = params.get("tau_E")
     tau_I = params.get("tau_I")
     E_L = params.get("E_L")
-    neuron_type = params.get("neuron_type")
-    if ("iaf_psc_exp" in neuron_type) or ("gif_psc_exp" in neuron_type):
-        tau_syn_ex = params.get("tau_syn_ex")
-        tau_syn_in = params.get("tau_syn_in")
-        amp_EE = max_psp_exp(tau_E, tau_syn_ex)
-        amp_EI = max_psp_exp(tau_E, tau_syn_in)
-        amp_IE = max_psp_exp(tau_I, tau_syn_ex)
-        amp_II = max_psp_exp(tau_I, tau_syn_in)
-    else:
-        amp_EE = 1.0
-        amp_EI = 1.0
-        amp_IE = 1.0
-        amp_II = 1.0
+    tau_syn_ex = params.get("tau_syn_ex")
+    tau_syn_in = params.get("tau_syn_in")
+    amp_EE = postsynaptic_current_to_potential(tau_E, tau_syn_ex)
+    amp_EI = postsynaptic_current_to_potential(tau_E, tau_syn_in)
+    amp_IE = postsynaptic_current_to_potential(tau_I, tau_syn_ex)
+    amp_II = postsynaptic_current_to_potential(tau_I, tau_syn_in)
 
     js = np.zeros((2, 2))
-    K_EE = N_E * ps[0, 0]
+    K_EE = N_E * baseline_conn_prob[0, 0]
     js[0, 0] = (V_th_E - E_L) * (K_EE**-0.5) * N**0.5 / amp_EE
-    js[0, 1] = -ge * js[0, 0] * ps[0, 0] * N_E * amp_EE / (ps[0, 1] * N_I * amp_EI)
-    K_IE = N_E * ps[1, 0]
+    js[0, 1] = -gei * js[0, 0] * baseline_conn_prob[0, 0] * N_E * amp_EE / (baseline_conn_prob[0, 1] * N_I * amp_EI)
+    K_IE = N_E * baseline_conn_prob[1, 0]
     js[1, 0] = gie * (V_th_I - E_L) * (K_IE**-0.5) * N**0.5 / amp_IE
-    js[1, 1] = -gi * js[1, 0] * ps[1, 0] * N_E * amp_IE / (ps[1, 1] * N_I * amp_II)
+    js[1, 1] = -gii * js[1, 0] * baseline_conn_prob[1, 0] * N_E * amp_IE / (baseline_conn_prob[1, 1] * N_I * amp_II)
     return js
 
 
-def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
-    """Calculate first pasage time between Vstart and Vtarget.
+def rheobase_current(tau_m, e_l, v_th, c_m):
+    """Rheobase current for a given membrane time constant and resting potential.
 
     Parameters
     ----------
@@ -113,56 +107,60 @@ def fpt(tau_m, e_l, i_e, c_m, vtarget, vstart):
         Membrane time constant [ms]
     e_l: float
         Resting potential [mV]
-    i_e: float
-        external current [pA]
+    v_th: float
+        Threshold potential [mV]
     c_m: float
         Membrane capacity [pF]
-    vtarget: float
-        target voltage [mV]
-    vstart: float
-        start voltage [mV]
 
     Returns
     -------
     float
-        first passage time [ms]
+        rheobase current [pA]
     """
-    inner = (vtarget - e_l - tau_m * i_e / c_m) / (
-        vstart - e_l - tau_m * i_e / c_m + small
-    )
-    if inner < 0:
-        return np.nan
-    else:
-        return -tau_m * np.log(inner)
+    return (v_th - e_l) * c_m / tau_m
 
 
-def v_fpt(tau_m, e_l, i_e, c_m, ttarget, vtarget, t_ref):
-    """Initial voltage to obtain a certain first passage time.
+def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
+    """Raster plot of spiketimes.
+
+    Plots raster plot of spiketimes withing given time limits and colors neurons according to colorgroups.
 
     Parameters
     ----------
-    tau_m: float
-        Membrane time constant [ms]
-    e_l: float
-        Resting potential [mV]
-    i_e: float
-        external current [pA]
-    c_m: float
-        Membrane capacity [pF]
-    ttarget: float
-        target first passage time [ms]
-    vtarget: float
-        target voltage [mV]
-    t_ref: float
-        refractory period [ms]
+    spiketimes: ndarray
+        2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+    tlim: list of floats (optional)
+        Time limits of plot: [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
+    colorgroups: list of tuples (optional)
+        Each element is a tuple in the format (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons in
+        given range, if None is given all neurons are black.
+    ax: axis object (optional)
+        If None a new figure is created, else the plot is added to the given axis.
+    markersize: float (optional)
+        Size of markers. (default: 0.5)
 
     Returns
     -------
-    float
-        initial voltage [mV]
+    axis object
+        Axis object with raster plot.
     """
-    return (
-        (vtarget - e_l - tau_m * i_e / c_m) * np.exp(ttarget / tau_m)
-        + e_l
-        + tau_m * i_e / c_m
-    )
+    if ax is None:
+        fig, ax = plt.subplots()
+    if tlim is None:
+        tlim = [min(spiketimes[0]), max(spiketimes[0])]
+    if colorgroups is None:
+        colorgroups = [("k", 0, max(spiketimes[1]))]
+    for color, start, stop in colorgroups:
+        ax.plot(
+            spiketimes[0][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+            spiketimes[1][np.logical_and(spiketimes[1] >= start, spiketimes[1] < stop)],
+            color=color,
+            marker=".",
+            linestyle="None",
+            markersize=markersize,
+        )
+    ax.set_xlim(tlim)
+    ax.set_ylim([0, max(spiketimes[1])])
+    ax.set_xlabel("Time [ms]")
+    ax.set_ylabel("Neuron ID")
+    return ax

From ac537fad7efac9e68c096157056f94660f35bca6 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 11:12:01 +0100
Subject: [PATCH 13/23] Change of attributes to private and entirely use
 Nest.random for initialization

---
 .../examples/EI_clustered_network/network.py  | 622 ++++++------------
 1 file changed, 218 insertions(+), 404 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 7e96eea08a..905b48ae58 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -22,20 +22,17 @@
 """PyNEST EI-clustered network: Network Class
 ---------------------------------------------
 
-Main file of the EI-clustered network defining the ``ClusteredNetwork`` class with functions to
-build and simulate the network.
+``ClusteredNetwork`` class with functions to build and simulate the EI-clustered network.
 """
 
 import pickle
 
-import numpy as np
-
-import general_helper
 import helper
 import nest
+import numpy as np
 
 
-class ClusteredNetwork(object):
+class ClusteredNetwork:
     """EI-clustered network objeect to build and simulate the network.
 
     Provides functions to create neuron populations, stimulation devices and recording devices for an
@@ -43,33 +40,38 @@ class ClusteredNetwork(object):
 
     Attributes
     ----------
-    params: dict
+    _params: dict
         Dictionary with parameters used to construct network.
-    Populations: list
+    _populations: list
         List of neuron population groups.
-    RecordingDevices: list
+    _recording_devices: list
         List of recording devices.
-    Currentsources: list
+    _currentsources: list
         List of current sources.
-    ModelBuildPipeline: list
+    _model_build_pipeline: list
         List of functions to build the network.
     """
 
-    def __init__(self, defaultValues, parameters):
+    def __init__(self, sim_dict, net_dict, stim_dict):
         """Initialize the ClusteredNetwork object.
 
+        Parameters are given and explained in the files network_params.py, sim_params.py and stimulus_params.py.
+
         Parameters
         ----------
-        defaultValues : module
-            A Module which contains the default configuration
-        parameters : dict
-            Dictionary with parameters which should be modified from their default values
+        sim_dict: dict
+            Dictionary with simulation parameters.
+        net_dict: dict
+            Dictionary with network parameters.
+        stim_dict: dict
+            Dictionary with stimulus parameters.
         """
-        self.params = general_helper.merge_params(parameters, defaultValues)
-        self.Populations = []
-        self.RecordingDevices = []
-        self.Currentsources = []
-        self.ModelBuildPipeline = [
+        # merge dictionaries of simulation, network and stimulus parameters
+        self._params = {**sim_dict, **net_dict, **stim_dict}  # loo
+        self._populations = []  # list of neuron population groups [E_pops, I_pops]
+        self._recording_devices = []
+        self._currentsources = []
+        self._model_build_pipeline = [
             self.setup_nest,
             self.create_populations,
             self.create_stimulation,
@@ -77,51 +79,16 @@ def __init__(self, defaultValues, parameters):
             self.connect,
         ]
 
-    @classmethod
-    def Clustered_Network_from_Rj_Rep(cls, Rj, Rep, defaultValues, parameters):
-        """Creates a ClusteredNetwork object from parameters Rj and Rep
-
-        Rj and Rep are used to calculate the parameters jplus (weight clustering) or pplus (probability clustering)
-
-        Parameters
-        ----------
-        Rj : float
-            Ratio excitatory to inhibitory clustering strength
-        Rep : float
-            Excitatory clustering strength
-        defaultValues : module
-            A Module which contains the default configuration
-        parameters : dict
-            Dictionary with parameters which should be modified from their default values
-
-        Returns
-        -------
-        ClusteredNetwork
-            ClusteredNetwork object
-
-        Raises
-        ----------
-        ValueError
-            If the clustering type is not recognized
-        """
-        params = general_helper.merge_params(parameters, defaultValues)
-        if params["clustering"] == "weight":
-            jep = Rep
-            jip = 1.0 + (jep - 1) * Rj
-            params["jplus"] = np.array([[jep, jip], [jip, jip]])
-        elif params["clustering"] == "probabilities":
-            pep = Rep
-            pip = 1.0 + (pep - 1) * Rj
-            params["pplus"] = np.array([[pep, pip], [pip, pip]])
+        if self._params["clustering"] == "weight":
+            jep = self._params["rep"]
+            jip = 1.0 + (jep - 1) * self._params["rj"]
+            self._params["jplus"] = np.array([[jep, jip], [jip, jip]])
+        elif self._params["clustering"] == "probabilities":
+            pep = self._params["rep"]
+            pip = 1.0 + (pep - 1) ** self._params["rj"]
+            self._params["pplus"] = np.array([[pep, pip], [pip, pip]])
         else:
             raise ValueError("Clustering type not recognized")
-        return cls(defaultValues, params)
-
-    def clean_network(self):
-        """Clears the object of all attributes."""
-        self.Populations = []
-        self.RecordingDevices = []
-        self.Currentsources = []
 
     def setup_nest(self):
         """Initializes the NEST kernel.
@@ -131,211 +98,91 @@ def setup_nest(self):
         """
         nest.ResetKernel()
         nest.set_verbosity("M_WARNING")
-        nest.local_num_threads = self.params.get("n_jobs", 1)
-        nest.resolution = self.params.get("dt")
-        self.params["randseed"] = self.params.get(
-            "randseed", np.random.randint(1000000)
-        )
-        nest.rng_seed = self.params.get("randseed")
-        # nest.print_time = True
+        nest.local_num_threads = self._params.get("n_vp", 4)
+        nest.resolution = self._params.get("dt")
+        self._params["randseed"] = self._params.get("randseed")
+        nest.rng_seed = self._params.get("randseed")
 
     def create_populations(self):
         """Create all neuron populations.
 
-        Q excitatory and inhibitory neuron populations with the parameters of the network are created.
+        n_clusters excitatory and inhibitory neuron populations with the parameters of the network are created.
         """
         # make sure number of clusters and units are compatible
-        assert (
-            self.params["N_E"] % self.params["Q"] == 0
-        ), "N_E needs to be evenly divisible by Q"
-        assert (
-            self.params["N_I"] % self.params["Q"] == 0
-        ), "N_I needs to be evenly divisible by Q"
-
-        N = self.params["N_E"] + self.params["N_I"]  # total units
-        try:
-            DistParams = self.params["DistParams"]
-        except AttributeError:
-            DistParams = {"distribution": "normal", "sigma": 0.0, "fraction": False}
-
-        if self.params["I_th_E"] is None:
-            I_xE = self.params["I_xE"]
+        if self._params["N_E"] % self._params["n_clusters"] != 0:
+            raise ValueError("N_E must be a multiple of Q")
+        if self._params["N_I"] % self._params["n_clusters"] != 0:
+            raise ValueError("N_E must be a multiple of Q")
+        if self._params["neuron_type"] != "iaf_psc_exp":
+            raise ValueError("Model only implemented for iaf_psc_exp neuron model")
+
+        if self._params["I_th_E"] is None:
+            I_xE = self._params["I_xE"]  # I_xE is the feed forward excitatory input in pA
         else:
-            I_xE = (
-                self.params["I_th_E"]
-                * (self.params["V_th_E"] - self.params["E_L"])
-                / self.params["tau_E"]
-                * self.params["C_m"]
+            I_xE = self._params["I_th_E"] * helper.rheobase_current(
+                self._params["tau_E"], self._params["E_L"], self._params["V_th_E"], self._params["C_m"]
             )
 
-        if self.params["I_th_I"] is None:
-            I_xI = self.params["I_xI"]
+        if self._params["I_th_I"] is None:
+            I_xI = self._params["I_xI"]
         else:
-            I_xI = (
-                self.params["I_th_I"]
-                * (self.params["V_th_I"] - self.params["E_L"])
-                / self.params["tau_I"]
-                * self.params["C_m"]
+            I_xI = self._params["I_th_I"] * helper.rheobase_current(
+                self._params["tau_I"], self._params["E_L"], self._params["V_th_I"], self._params["C_m"]
             )
 
         E_neuron_params = {
-            "E_L": self.params["E_L"],
-            "C_m": self.params["C_m"],
-            "tau_m": self.params["tau_E"],
-            "t_ref": self.params["t_ref"],
-            "V_th": self.params["V_th_E"],
-            "V_reset": self.params["V_r"],
-            "I_e": I_xE,
+            "E_L": self._params["E_L"],
+            "C_m": self._params["C_m"],
+            "tau_m": self._params["tau_E"],
+            "t_ref": self._params["t_ref"],
+            "V_th": self._params["V_th_E"],
+            "V_reset": self._params["V_r"],
+            "I_e": I_xE
+            if self._params["delta_I_xE"] == 0
+            else I_xE * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2),
+            "tau_syn_ex": self._params["tau_syn_ex"],
+            "tau_syn_in": self._params["tau_syn_in"],
+            "V_m": self._params["V_m"]
+            if not self._params["V_m"] == "rand"
+            else self._params["V_th_E"] - 20 * nest.random.lognormal(0, 1),
         }
         I_neuron_params = {
-            "E_L": self.params["E_L"],
-            "C_m": self.params["C_m"],
-            "tau_m": self.params["tau_I"],
-            "t_ref": self.params["t_ref"],
-            "V_th": self.params["V_th_I"],
-            "V_reset": self.params["V_r"],
-            "I_e": I_xI,
+            "E_L": self._params["E_L"],
+            "C_m": self._params["C_m"],
+            "tau_m": self._params["tau_I"],
+            "t_ref": self._params["t_ref"],
+            "V_th": self._params["V_th_I"],
+            "V_reset": self._params["V_r"],
+            "I_e": I_xI
+            if self._params["delta_I_xE"] == 0
+            else I_xI * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2),
+            "tau_syn_ex": self._params["tau_syn_ex"],
+            "tau_syn_in": self._params["tau_syn_in"],
+            "V_m": self._params["V_m"]
+            if not self._params["V_m"] == "rand"
+            else self._params["V_th_I"] - 20 * nest.random.lognormal(0, 1),
         }
-        if "iaf_psc_exp" in self.params["neuron_type"]:
-            E_neuron_params["tau_syn_ex"] = self.params["tau_syn_ex"]
-            E_neuron_params["tau_syn_in"] = self.params["tau_syn_in"]
-            I_neuron_params["tau_syn_in"] = self.params["tau_syn_in"]
-            I_neuron_params["tau_syn_ex"] = self.params["tau_syn_ex"]
-
-            # iaf_psc_exp allows stochasticity, if not used - ignore
-            try:
-                if self.params["delta_"] is not None:
-                    E_neuron_params["delta"] = self.params["delta_"]
-                    I_neuron_params["delta"] = self.params["delta_"]
-                if self.params["rho"] is not None:
-                    E_neuron_params["rho"] = self.params["rho"]
-                    I_neuron_params["rho"] = self.params["rho"]
-            except KeyError:
-                pass
-        else:
-            assert (
-                "iaf_psc_exp" in self.params["neuron_type"]
-            ), "iaf_psc_exp neuron model is the only implemented model"
-
-            # create the neuron populations
-        E_pops = []
-        I_pops = []
-        for q in range(self.params["Q"]):
-            E_pops.append(
-                nest.Create(
-                    self.params["neuron_type"],
-                    int(self.params["N_E"] / self.params["Q"]),
-                )
-            )
-            nest.SetStatus(E_pops[-1], E_neuron_params)
-        for q in range(self.params["Q"]):
-            I_pops.append(
-                nest.Create(
-                    self.params["neuron_type"],
-                    int(self.params["N_I"] / self.params["Q"]),
-                )
-            )
-            nest.SetStatus(I_pops[-1], I_neuron_params)
-
-        if self.params["delta_I_xE"] > 0:
-            for E_pop in E_pops:
-                I_xEs = nest.GetStatus(E_pop, "I_e")
-                nest.SetStatus(
-                    E_pop,
-                    [
-                        {
-                            "I_e": (
-                                1
-                                - 0.5 * self.params["delta_I_xE"]
-                                + np.random.rand() * self.params["delta_I_xE"]
-                            )
-                            * ixe
-                        }
-                        for ixe in I_xEs
-                    ],
-                )
-
-        if self.params["delta_I_xI"] > 0:
-            for I_pop in I_pops:
-                I_xIs = nest.GetStatus(I_pop, "I_e")
-                nest.SetStatus(
-                    I_pop,
-                    [
-                        {
-                            "I_e": (
-                                1
-                                - 0.5 * self.params["delta_I_xI"]
-                                + np.random.rand() * self.params["delta_I_xI"]
-                            )
-                            * ixi
-                        }
-                        for ixi in I_xIs
-                    ],
-                )
-        if self.params["V_m"] == "rand":
-            T_0_E = self.params["t_ref"] + helper.fpt(
-                self.params["tau_E"],
-                self.params["E_L"],
-                I_xE,
-                self.params["C_m"],
-                self.params["V_th_E"],
-                self.params["V_r"],
-            )
-            if np.isnan(T_0_E):
-                T_0_E = 10.0
-            for E_pop in E_pops:
-                nest.SetStatus(
-                    E_pop,
-                    [
-                        {
-                            "V_m": helper.v_fpt(
-                                self.params["tau_E"],
-                                self.params["E_L"],
-                                I_xE,
-                                self.params["C_m"],
-                                T_0_E * np.random.rand(),
-                                self.params["V_th_E"],
-                                self.params["t_ref"],
-                            )
-                        }
-                        for i in range(len(E_pop))
-                    ],
-                )
-
-            T_0_I = self.params["t_ref"] + helper.fpt(
-                self.params["tau_I"],
-                self.params["E_L"],
-                I_xI,
-                self.params["C_m"],
-                self.params["V_th_I"],
-                self.params["V_r"],
-            )
-            if np.isnan(T_0_I):
-                T_0_I = 10.0
-            for I_pop in I_pops:
-                nest.SetStatus(
-                    I_pop,
-                    [
-                        {
-                            "V_m": helper.v_fpt(
-                                self.params["tau_I"],
-                                self.params["E_L"],
-                                I_xI,
-                                self.params["C_m"],
-                                T_0_I * np.random.rand(),
-                                self.params["V_th_E"],
-                                self.params["t_ref"],
-                            )
-                        }
-                        for i in range(len(I_pop))
-                    ],
-                )
-        else:
-            nest.SetStatus(
-                nest.NodeCollection([x for x in range(1, N + 1)]),
-                [{"V_m": self.params["V_m"]} for i in range(N)],
-            )
-        self.Populations = [E_pops, I_pops]
+
+        # iaf_psc_exp allows stochasticity, if not used - ignore
+        if (self._params.get("delta") is not None) and (self._params.get("rho") is not None):
+            E_neuron_params["delta"] = self._params["delta"]
+            I_neuron_params["delta"] = self._params["delta"]
+            E_neuron_params["rho"] = self._params["rho"]
+            I_neuron_params["rho"] = self._params["rho"]
+
+        # create the neuron populations
+        pop_size_E = self._params["N_E"] // self._params["n_clusters"]
+        pop_size_I = self._params["N_I"] // self._params["n_clusters"]
+        E_pops = [
+            nest.Create(self._params["neuron_type"], n=pop_size_E, params=E_neuron_params)
+            for _ in range(self._params["n_clusters"])
+        ]
+        I_pops = [
+            nest.Create(self._params["neuron_type"], n=pop_size_I, params=I_neuron_params)
+            for _ in range(self._params["n_clusters"])
+        ]
+
+        self._populations = [E_pops, I_pops]
 
     def connect(self):
         """Connect the excitatory and inhibitory populations with each other in the EI-clustered scheme
@@ -345,18 +192,13 @@ def connect(self):
         ValueError
             If the clustering method is not recognized
         """
-        # if CLustering is set to "Weight" or is not set at all or is not in dictionary, use the connect_weight function
-        try:
-            if self.params["clustering"] == "weight":
-                self.connect_weight()
-            elif self.params["clustering"] == "probabilities":
-                self.connect_probabilities()
-            else:
-                raise ValueError(
-                    "Clustering method %s not implemented" % self.params["clustering"]
-                )
-        except KeyError:
+
+        if "clustering" not in self._params or self._params["clustering"] == "weight":
             self.connect_weight()
+        elif self._params["clustering"] == "probabilities":
+            self.connect_probabilities()
+        else:
+            raise ValueError("Clustering method %s not implemented" % self._params["clustering"])
 
     def connect_probabilities(self):
         """Connect the clusters with a probability EI-clustered scheme
@@ -366,25 +208,23 @@ def connect_probabilities(self):
         connections between the clusters. The weights are calculated so that the total input to a neuron
         is balanced.
         """
-        #  self.Populations[0] -> Excitatory population
-        #  self.Populations[1] -> Inhibitory population
+        #  self._populations[0] -> Excitatory population
+        #  self._populations[1] -> Inhibitory population
         # connectivity parameters
-        js = self.params["js"]  # connection weights
-        N = self.params["N_E"] + self.params["N_I"]  # total units
+        js = self._params["js"]  # connection weights
+        N = self._params["N_E"] + self._params["N_I"]  # total units
         if np.isnan(js).any():
-            js = helper.calc_js(self.params)
-        js *= self.params["s"]
+            js = helper.calculate_RBN_weights(self._params)
+        js *= self._params["s"]
 
-        if self.params["Q"] > 1:
-            pminus = (self.params["Q"] - self.params["pplus"]) / float(
-                self.params["Q"] - 1
-            )
+        if self._params["n_clusters"] > 1:
+            pminus = (self._params["n_clusters"] - self._params["pplus"]) / float(self._params["n_clusters"] - 1)
         else:
-            self.params["pplus"] = np.ones((2, 2))
+            self._params["pplus"] = np.ones((2, 2))
             pminus = np.ones((2, 2))
 
-        p_plus = self.params["pplus"] * self.params["ps"]
-        p_minus = pminus * self.params["ps"]
+        p_plus = self._params["pplus"] * self._params["baseline_conn_prob"]
+        p_minus = pminus * self._params["baseline_conn_prob"]
 
         iterations = np.ones((2, 2), dtype=int)
         # test if any of the probabilities is larger than 1
@@ -403,14 +243,12 @@ def connect_probabilities(self):
         # define the synapses and connect the populations
         # EE
         j_ee = js[0, 0] / np.sqrt(N)
-        nest.CopyModel(
-            "static_synapse", "EE", {"weight": j_ee, "delay": self.params["delay"]}
-        )
+        nest.CopyModel("static_synapse", "EE", {"weight": j_ee, "delay": self._params["delay"]})
 
-        if self.params["fixed_indegree"]:
-            K_EE_plus = int(p_plus[0, 0] * self.params["N_E"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_EE_plus = int(p_plus[0, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_EE+: ", K_EE_plus)
-            K_EE_minus = int(p_minus[0, 0] * self.params["N_E"] / self.params["Q"])
+            K_EE_minus = int(p_minus[0, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_EE-: ", K_EE_minus)
             conn_params_EE_plus = {
                 "rule": "fixed_indegree",
@@ -438,8 +276,8 @@ def connect_probabilities(self):
                 "allow_autapses": False,
                 "allow_multapses": True,
             }
-        for i, pre in enumerate(self.Populations[0]):
-            for j, post in enumerate(self.Populations[0]):
+        for i, pre in enumerate(self._populations[0]):
+            for j, post in enumerate(self._populations[0]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[0, 0]):
@@ -449,14 +287,12 @@ def connect_probabilities(self):
 
         # EI
         j_ei = js[0, 1] / np.sqrt(N)
-        nest.CopyModel(
-            "static_synapse", "EI", {"weight": j_ei, "delay": self.params["delay"]}
-        )
+        nest.CopyModel("static_synapse", "EI", {"weight": j_ei, "delay": self._params["delay"]})
 
-        if self.params["fixed_indegree"]:
-            K_EI_plus = int(p_plus[0, 1] * self.params["N_I"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_EI_plus = int(p_plus[0, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_EI+: ", K_EI_plus)
-            K_EI_minus = int(p_minus[0, 1] * self.params["N_I"] / self.params["Q"])
+            K_EI_minus = int(p_minus[0, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_EI-: ", K_EI_minus)
             conn_params_EI_plus = {
                 "rule": "fixed_indegree",
@@ -484,8 +320,8 @@ def connect_probabilities(self):
                 "allow_autapses": False,
                 "allow_multapses": True,
             }
-        for i, pre in enumerate(self.Populations[1]):
-            for j, post in enumerate(self.Populations[0]):
+        for i, pre in enumerate(self._populations[1]):
+            for j, post in enumerate(self._populations[0]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[0, 1]):
@@ -495,14 +331,12 @@ def connect_probabilities(self):
 
         # IE
         j_ie = js[1, 0] / np.sqrt(N)
-        nest.CopyModel(
-            "static_synapse", "IE", {"weight": j_ie, "delay": self.params["delay"]}
-        )
+        nest.CopyModel("static_synapse", "IE", {"weight": j_ie, "delay": self._params["delay"]})
 
-        if self.params["fixed_indegree"]:
-            K_IE_plus = int(p_plus[1, 0] * self.params["N_E"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_IE_plus = int(p_plus[1, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_IE+: ", K_IE_plus)
-            K_IE_minus = int(p_minus[1, 0] * self.params["N_E"] / self.params["Q"])
+            K_IE_minus = int(p_minus[1, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_IE-: ", K_IE_minus)
             conn_params_IE_plus = {
                 "rule": "fixed_indegree",
@@ -530,8 +364,8 @@ def connect_probabilities(self):
                 "allow_autapses": False,
                 "allow_multapses": True,
             }
-        for i, pre in enumerate(self.Populations[0]):
-            for j, post in enumerate(self.Populations[1]):
+        for i, pre in enumerate(self._populations[0]):
+            for j, post in enumerate(self._populations[1]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[1, 0]):
@@ -541,14 +375,12 @@ def connect_probabilities(self):
 
         # II
         j_ii = js[1, 1] / np.sqrt(N)
-        nest.CopyModel(
-            "static_synapse", "II", {"weight": j_ii, "delay": self.params["delay"]}
-        )
+        nest.CopyModel("static_synapse", "II", {"weight": j_ii, "delay": self._params["delay"]})
 
-        if self.params["fixed_indegree"]:
-            K_II_plus = int(p_plus[1, 1] * self.params["N_I"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_II_plus = int(p_plus[1, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_II+: ", K_II_plus)
-            K_II_minus = int(p_minus[1, 1] * self.params["N_I"] / self.params["Q"])
+            K_II_minus = int(p_minus[1, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_II-: ", K_II_minus)
             conn_params_II_plus = {
                 "rule": "fixed_indegree",
@@ -576,8 +408,8 @@ def connect_probabilities(self):
                 "allow_autapses": False,
                 "allow_multapses": True,
             }
-        for i, pre in enumerate(self.Populations[1]):
-            for j, post in enumerate(self.Populations[1]):
+        for i, pre in enumerate(self._populations[1]):
+            for j, post in enumerate(self._populations[1]):
                 if i == j:
                     # same cluster
                     for n in range(iterations[1, 1]):
@@ -593,24 +425,22 @@ def connect_weight(self):
         connections between the clusters. The weights are calculated so that the total input to a neuron
         is balanced.
         """
-        #  self.Populations[0] -> Excitatory population
-        #  self.Populations[1] -> Inhibitory population
-        # connectivity parameters
-        js = self.params["js"]  # connection weights
-        N = self.params["N_E"] + self.params["N_I"]  # total units
+        #  self._populations[0] -> Excitatory population
+        #  self._populations[1] -> Inhibitory population
+
+        N = self._params["N_E"] + self._params["N_I"]  # total units
 
         # if js are not given compute them so that sqrt(K) spikes equal v_thr-E_L and rows are balanced
-        if np.isnan(js).any():
-            js = helper.calc_js(self.params)
-        js *= self.params["s"]
+        # if any of the js is nan or not given
+        if self._params.get("js") is None or np.isnan(self._params.get("js")).any():
+            js = helper.calculate_RBN_weights(self._params)
+        js *= self._params["s"]
 
         # jminus is calculated so that row sums remain constant
-        if self.params["Q"] > 1:
-            jminus = (self.params["Q"] - self.params["jplus"]) / float(
-                self.params["Q"] - 1
-            )
+        if self._params["n_clusters"] > 1:
+            jminus = (self._params["n_clusters"] - self._params["jplus"]) / float(self._params["n_clusters"] - 1)
         else:
-            self.params["jplus"] = np.ones((2, 2))
+            self._params["jplus"] = np.ones((2, 2))
             jminus = np.ones((2, 2))
 
         # define the synapses and connect the populations
@@ -620,17 +450,17 @@ def connect_weight(self):
             "static_synapse",
             "EE_plus",
             {
-                "weight": self.params["jplus"][0, 0] * j_ee,
-                "delay": self.params["delay"],
+                "weight": self._params["jplus"][0, 0] * j_ee,
+                "delay": self._params["delay"],
             },
         )
         nest.CopyModel(
             "static_synapse",
             "EE_minus",
-            {"weight": jminus[0, 0] * j_ee, "delay": self.params["delay"]},
+            {"weight": jminus[0, 0] * j_ee, "delay": self._params["delay"]},
         )
-        if self.params["fixed_indegree"]:
-            K_EE = int(self.params["ps"][0, 0] * self.params["N_E"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_EE = int(self._params["baseline_conn_prob"][0, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_EE: ", K_EE)
             conn_params_EE = {
                 "rule": "fixed_indegree",
@@ -642,12 +472,12 @@ def connect_weight(self):
         else:
             conn_params_EE = {
                 "rule": "pairwise_bernoulli",
-                "p": self.params["ps"][0, 0],
+                "p": self._params["baseline_conn_prob"][0, 0],
                 "allow_autapses": False,
                 "allow_multapses": False,
             }
-        for i, pre in enumerate(self.Populations[0]):
-            for j, post in enumerate(self.Populations[0]):
+        for i, pre in enumerate(self._populations[0]):
+            for j, post in enumerate(self._populations[0]):
                 if i == j:
                     # same cluster
                     nest.Connect(pre, post, conn_params_EE, "EE_plus")
@@ -660,17 +490,17 @@ def connect_weight(self):
             "static_synapse",
             "EI_plus",
             {
-                "weight": j_ei * self.params["jplus"][0, 1],
-                "delay": self.params["delay"],
+                "weight": j_ei * self._params["jplus"][0, 1],
+                "delay": self._params["delay"],
             },
         )
         nest.CopyModel(
             "static_synapse",
             "EI_minus",
-            {"weight": j_ei * jminus[0, 1], "delay": self.params["delay"]},
+            {"weight": j_ei * jminus[0, 1], "delay": self._params["delay"]},
         )
-        if self.params["fixed_indegree"]:
-            K_EI = int(self.params["ps"][0, 1] * self.params["N_I"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_EI = int(self._params["baseline_conn_prob"][0, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_EI: ", K_EI)
             conn_params_EI = {
                 "rule": "fixed_indegree",
@@ -681,12 +511,12 @@ def connect_weight(self):
         else:
             conn_params_EI = {
                 "rule": "pairwise_bernoulli",
-                "p": self.params["ps"][0, 1],
+                "p": self._params["baseline_conn_prob"][0, 1],
                 "allow_autapses": False,
                 "allow_multapses": False,
             }
-        for i, pre in enumerate(self.Populations[1]):
-            for j, post in enumerate(self.Populations[0]):
+        for i, pre in enumerate(self._populations[1]):
+            for j, post in enumerate(self._populations[0]):
                 if i == j:
                     # same cluster
                     nest.Connect(pre, post, conn_params_EI, "EI_plus")
@@ -698,18 +528,18 @@ def connect_weight(self):
             "static_synapse",
             "IE_plus",
             {
-                "weight": j_ie * self.params["jplus"][1, 0],
-                "delay": self.params["delay"],
+                "weight": j_ie * self._params["jplus"][1, 0],
+                "delay": self._params["delay"],
             },
         )
         nest.CopyModel(
             "static_synapse",
             "IE_minus",
-            {"weight": j_ie * jminus[1, 0], "delay": self.params["delay"]},
+            {"weight": j_ie * jminus[1, 0], "delay": self._params["delay"]},
         )
 
-        if self.params["fixed_indegree"]:
-            K_IE = int(self.params["ps"][1, 0] * self.params["N_E"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_IE = int(self._params["baseline_conn_prob"][1, 0] * self._params["N_E"] / self._params["n_clusters"])
             print("K_IE: ", K_IE)
             conn_params_IE = {
                 "rule": "fixed_indegree",
@@ -720,12 +550,12 @@ def connect_weight(self):
         else:
             conn_params_IE = {
                 "rule": "pairwise_bernoulli",
-                "p": self.params["ps"][1, 0],
+                "p": self._params["baseline_conn_prob"][1, 0],
                 "allow_autapses": False,
                 "allow_multapses": False,
             }
-        for i, pre in enumerate(self.Populations[0]):
-            for j, post in enumerate(self.Populations[1]):
+        for i, pre in enumerate(self._populations[0]):
+            for j, post in enumerate(self._populations[1]):
                 if i == j:
                     # same cluster
                     nest.Connect(pre, post, conn_params_IE, "IE_plus")
@@ -738,17 +568,17 @@ def connect_weight(self):
             "static_synapse",
             "II_plus",
             {
-                "weight": j_ii * self.params["jplus"][1, 1],
-                "delay": self.params["delay"],
+                "weight": j_ii * self._params["jplus"][1, 1],
+                "delay": self._params["delay"],
             },
         )
         nest.CopyModel(
             "static_synapse",
             "II_minus",
-            {"weight": j_ii * jminus[1, 1], "delay": self.params["delay"]},
+            {"weight": j_ii * jminus[1, 1], "delay": self._params["delay"]},
         )
-        if self.params["fixed_indegree"]:
-            K_II = int(self.params["ps"][1, 1] * self.params["N_I"] / self.params["Q"])
+        if self._params["fixed_indegree"]:
+            K_II = int(self._params["baseline_conn_prob"][1, 1] * self._params["N_I"] / self._params["n_clusters"])
             print("K_II: ", K_II)
             conn_params_II = {
                 "rule": "fixed_indegree",
@@ -759,39 +589,36 @@ def connect_weight(self):
         else:
             conn_params_II = {
                 "rule": "pairwise_bernoulli",
-                "p": self.params["ps"][1, 1],
+                "p": self._params["baseline_conn_prob"][1, 1],
                 "allow_autapses": False,
                 "allow_multapses": False,
             }
-        for i, pre in enumerate(self.Populations[1]):
-            for j, post in enumerate(self.Populations[1]):
+        for i, pre in enumerate(self._populations[1]):
+            for j, post in enumerate(self._populations[1]):
                 if i == j:
                     # same cluster
                     nest.Connect(pre, post, conn_params_II, "II_plus")
                 else:
                     nest.Connect(pre, post, conn_params_II, "II_minus")
-        # print('Js: ', js / np.sqrt(N))
 
     def create_stimulation(self):
         """Create a current source and connect it to clusters."""
-        if self.params["stim_clusters"] is not None:
-            stim_amp = self.params[
-                "stim_amp"
-            ]  # amplitude of the stimulation current in pA
-            stim_starts = self.params["stim_starts"]  # list of stimulation start times
-            stim_ends = self.params["stim_ends"]  # list of stimulation end times
+        if self._params["stim_clusters"] is not None:
+            stim_amp = self._params["stim_amp"]  # amplitude of the stimulation current in pA
+            stim_starts = self._params["stim_starts"]  # list of stimulation start times
+            stim_ends = self._params["stim_ends"]  # list of stimulation end times
             amplitude_values = []
             amplitude_times = []
             for start, end in zip(stim_starts, stim_ends):
-                amplitude_times.append(start + self.params["warmup"])
+                amplitude_times.append(start + self._params["warmup"])
                 amplitude_values.append(stim_amp)
-                amplitude_times.append(end + self.params["warmup"])
+                amplitude_times.append(end + self._params["warmup"])
                 amplitude_values.append(0.0)
-            self.Currentsources = [nest.Create("step_current_generator")]
-            for stim_cluster in self.params["stim_clusters"]:
-                nest.Connect(self.Currentsources[0], self.Populations[0][stim_cluster])
+            self._currentsources = [nest.Create("step_current_generator")]
+            for stim_cluster in self._params["stim_clusters"]:
+                nest.Connect(self._currentsources[0], self._populations[0][stim_cluster])
             nest.SetStatus(
-                self.Currentsources[0],
+                self._currentsources[0],
                 {
                     "amplitude_times": amplitude_times,
                     "amplitude_values": amplitude_values,
@@ -801,45 +628,40 @@ def create_stimulation(self):
     def create_recording_devices(self):
         """Creates a spike recorder
 
-        Create and connect a spike recorder to all neuron populations in self.Populations.
+        Create and connect a spike recorder to all neuron populations in self._populations.
         """
-        self.RecordingDevices = [nest.Create("spike_recorder")]
-        self.RecordingDevices[0].record_to = "memory"
+        self._recording_devices = [nest.Create("spike_recorder")]
+        self._recording_devices[0].record_to = "memory"
 
-        all_units = self.Populations[0][0]
-        for E_pop in self.Populations[0][1:]:
+        all_units = self._populations[0][0]
+        for E_pop in self._populations[0][1:]:
             all_units += E_pop
-        for I_pop in self.Populations[1]:
+        for I_pop in self._populations[1]:
             all_units += I_pop
-        nest.Connect(all_units, self.RecordingDevices[0], "all_to_all")  # Spikerecorder
+        nest.Connect(all_units, self._recording_devices[0], "all_to_all")  # Spikerecorder
 
     def set_model_build_pipeline(self, Pipeline):
-        """Set ModelBuildPipeline
+        """Set _model_build_pipeline
 
         Parameters
         ----------
         Pipeline: list
             ordered list of functions executed to build the network model
         """
-        self.ModelBuildPipeline = Pipeline
+        self._model_build_pipeline = Pipeline
 
     def setup_network(self):
         """Setup network in NEST
 
         Initializes NEST and creates the network in NEST, ready to be simulated.
-        Functions saved in ModelBuildPipeline are executed.
-        nest.Prepare is executed in this function.
+        Functions saved in _model_build_pipeline are executed.
         """
-        for func in self.ModelBuildPipeline:
+        for func in self._model_build_pipeline:
             func()
-        nest.Prepare()
 
     def simulate(self):
         """Simulates network for a period of warmup+simtime"""
-        if self.params["warmup"] + self.params["simtime"] <= 0.1:
-            pass
-        else:
-            nest.Run(self.params["warmup"] + self.params["simtime"])
+        nest.Simulate(self._params["warmup"] + self._params["simtime"])
 
     def get_recordings(self):
         """Extract spikes from Spikerecorder
@@ -853,15 +675,13 @@ def get_recordings(self):
         spiketimes: ndarray
             2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
         """
-        events = nest.GetStatus(self.RecordingDevices[0], "events")[0]
+        events = nest.GetStatus(self._recording_devices[0], "events")[0]
         # convert them to the format accepted by spiketools
-        spiketimes = np.append(
-            events["times"][None, :], events["senders"][None, :], axis=0
-        )
+        spiketimes = np.append(events["times"][None, :], events["senders"][None, :], axis=0)
         spiketimes[1] -= 1
         # remove the pre warmup spikes
-        spiketimes = spiketimes[:, spiketimes[0] >= self.params["warmup"]]
-        spiketimes[0] -= self.params["warmup"]
+        spiketimes = spiketimes[:, spiketimes[0] >= self._params["warmup"]]
+        spiketimes[0] -= self._params["warmup"]
         return spiketimes
 
     def get_parameter(self):
@@ -871,7 +691,7 @@ def get_parameter(self):
         dict
             Dictionary with all parameters of the network and the simulation.
         """
-        return self.params
+        return self._params
 
     def create_and_simulate(self):
         """Create and simulate the EI-clustered network.
@@ -902,14 +722,10 @@ def get_firing_rates(self, spiketimes=None):
         """
         if spiketimes is None:
             spiketimes = self.get_recordings()
-        e_count = spiketimes[:, spiketimes[1] < self.params["N_E"]].shape[1]
-        i_count = spiketimes[:, spiketimes[1] >= self.params["N_E"]].shape[1]
-        e_rate = (
-            e_count / float(self.params["N_E"]) / float(self.params["simtime"]) * 1000.0
-        )
-        i_rate = (
-            i_count / float(self.params["N_I"]) / float(self.params["simtime"]) * 1000.0
-        )
+        e_count = spiketimes[:, spiketimes[1] < self._params["N_E"]].shape[1]
+        i_count = spiketimes[:, spiketimes[1] >= self._params["N_E"]].shape[1]
+        e_rate = e_count / float(self._params["N_E"]) / float(self._params["simtime"]) * 1000.0
+        i_rate = i_count / float(self._params["N_I"]) / float(self._params["simtime"]) * 1000.0
         return e_rate, i_rate
 
     def set_I_x(self, I_XE, I_XI):
@@ -924,10 +740,10 @@ def set_I_x(self, I_XE, I_XI):
         I_XI: float
             extra DC current for inhibitory neurons [pA]
         """
-        for E_pop in self.Populations[0]:
+        for E_pop in self._populations[0]:
             I_e_loc = E_pop.get("I_e")
             E_pop.set({"I_e": I_e_loc + I_XE})
-        for I_pop in self.Populations[1]:
+        for I_pop in self._populations[1]:
             I_e_loc = I_pop.get("I_e")
             I_pop.set({"I_e": I_e_loc + I_XI})
 
@@ -957,11 +773,9 @@ def get_simulation(self, PathSpikes=None):
         if PathSpikes is not None:
             with open(PathSpikes, "wb") as outfile:
                 pickle.dump(spiketimes, outfile)
-
-        nest.Cleanup()
         return {
             "e_rate": e_rate,
             "i_rate": i_rate,
-            "params": self.get_parameter(),
+            "_params": self.get_parameter(),
             "spiketimes": spiketimes,
         }

From 1f7173c279a82f59d63dd2e58a8012f4d40dea13 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 11:12:28 +0100
Subject: [PATCH 14/23] Removed parameters

---
 .../EI_clustered_network/run_simulation.py    | 55 ++++---------------
 1 file changed, 10 insertions(+), 45 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 9cc1519704..2d046400a4 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -27,63 +27,28 @@
 """
 
 import matplotlib.pyplot as plt
-import numpy as np
-
-import default_parameters as default
-import general_helper
 import network
+from helper import raster_plot
+from network_params import net_dict
+from sim_params import sim_dict
+from stimulus_params import stim_dict
 
 if __name__ == "__main__":
-    # set the parameters for the simulation
-    params = {
-        "n_jobs": 12,  # number of threads used for parallelization in NEST
-        "N_E": 4000,  # number of excitatory neurons
-        "N_I": 1000,  # number of inhibitory neurons
-        "E_L": 0.0,  # leak potential
-        "V_th_E": 20.0,  # threshold potential for excitatory neurons
-        "V_th_I": 20.0,  # threshold potential for inhibitory neurons
-        "V_r": 0.0,  # reset potential
-        "C_m": 1.0,  # membrane capacity
-        "tau_E": 20.0,  # membrane time constant for excitatory neurons
-        "tau_I": 10.0,  # membrane time constant for inhibitory neurons
-        "t_ref": 5.0,  # refractory period
-        "ps": np.array([[0.2, 0.5], [0.5, 0.5]]),  # connection probabilities
-        "ge": 1.2,  # excitatory synaptic weight factor
-        "I_th_E": 1.25,  # Background current for excitatory neurons in multiples of I_rheobase
-        "I_th_I": 0.78,  # Background current for inhibitory neurons in multiples of I_rheobase
-        "dt": 0.1,  # simulation time step
-        "neuron_type": "iaf_psc_exp",  # neuron model used in NEST iaf and gif are available
-        "simtime": 10000,  # simulation time in ms
-        "warmup": 1000,  # warmup time in ms (time before recording starts)
-        "Q": 20,  # number of clusters
-        "clustering": "weight",  # type of clustering used 'weight' or
-        "stim_clusters": [2, 3, 4],  # list of clusters which are stimulated
-        "stim_starts": [2000, 6000],  # list of stimulation start times
-        "stim_ends": [3500, 7500],  # list of stimulation end times
-        "stim_amp": 0.15,  # amplitude of the stimulation current in pA
-        # 'probabilities'
-    }
-
-    Rj = 0.82  # ration excitatory to inhibitory clustering
-    Rep = 6.0  # excitatory clustering strength
-
     # Creates object which creates the EI clustered network in NEST
-    EI_Network = network.ClusteredNetwork.Clustered_Network_from_Rj_Rep(
-        Rj, Rep, default, params
-    )
+    EI_Network = network.ClusteredNetwork(sim_dict, net_dict, stim_dict)
 
     # Runs the simulation and returns the spiketimes
     # get simulation initializes the network in NEST and runs the simulation
     # it returns a dict with the average rates, the spiketimes and the used parameters
     Result = EI_Network.get_simulation()
-    ax = general_helper.raster_plot(
+    ax = raster_plot(
         Result["spiketimes"],
-        tlim=(0, params["simtime"]),
+        tlim=(0, sim_dict["simtime"]),
         colorgroups=[
-            ("k", 0, params["N_E"]),
-            ("darkred", params["N_E"], params["N_E"] + params["N_I"]),
+            ("k", 0, net_dict["N_E"]),
+            ("darkred", net_dict["N_E"], net_dict["N_E"] + net_dict["N_I"]),
         ],
     )
-    plt.savefig("Rasterplot.png")
+    plt.savefig("clustered_ei_raster.png")
     print("Firing rate of excitatory neurons: " + str(Result["e_rate"]) + " Spikes/s")
     print("Firing rate of inhibitory neurons: " + str(Result["i_rate"]) + " Spikes/s")

From ffb332321c62ae329ef58063f5f9b7e726cbaae6 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 11:13:03 +0100
Subject: [PATCH 15/23] Update to fit new structure

---
 pynest/examples/EI_clustered_network/README.rst | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index 9368b868c4..ca6111f334 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -6,7 +6,7 @@ This is PyNEST implementation of the EI-clustered circuit model described by Ros
 .. figure:: /static/img/pynest/EI_clustered_network_schematic.png
    :alt: EI-clustered circuit model.
 
-   Schematic of the EI-clustered circuit model. The network consists of Q clusters with one excitatory and one inhibitory population each.
+   Schematic of the EI-clustered circuit model. The network consists of n_clusters clusters with one excitatory and one inhibitory population each.
 
 Citing this code
 ----------------
@@ -18,9 +18,10 @@ File structure
 
 * :doc:`run_simulation.py <run_simulation>`: an example script to try out the EI-clustered circuit model
 * :doc:`network.py <network>`: the main Network class with functions to build and simulate the network
-* :doc:`helper.py <helper>`: helper functions for calculation of synaptic weights and currents
-* :doc:`general_helper.py <general_helper>`: helper functions to merge dictionaries and plot function for raster plots
-* :doc:`default_parameters.py <default_parameters>`: default parameters for the network and simulation
+* :doc:`helper.py <helper>`: helper functions for calculation of synaptic weights and currents and plot function for raster plots
+* :doc:`network_params.py <network_params>`: network and neuron parameters
+* :doc:`stimulus_params.py <stimulus_params>`: parameters for optional external stimulation
+* :doc:`sim_params.py <sim_params>`: simulation parameters
 
 Running the simulation
 ----------------------
@@ -29,13 +30,14 @@ Running the simulation
 
    python run_simulation.py
 
-A raster plot of the network activity is saved as ``Rasterplot.png``.
+A raster plot of the network activity is saved as ``clustered_ei_raster.png``.
 
 The code can be parallelized by using multiple threads during the NEST simulation.
-This can be done by setting the parameter ``n_jobs`` in the ``run_simulation.py`` script.
+This can be done by setting the parameter ``n_vp`` in the ``run_simulation.py`` script.
 
 Contributions to this PyNEST model implementation
---------------------------------------------------------------
+-------------------------------------------------
+
 2023: inital bersion of code and documentation by Felix J. Schmitt, Vahid Rostami and Martin Nawrot.
 
 Acknowledgments

From 61d37756f21fb72cf4a6aa787d5595bda81e1d7a Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 13:07:17 +0100
Subject: [PATCH 16/23] Corrected typo

---
 pynest/examples/EI_clustered_network/README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index ca6111f334..63e0949f5c 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -38,7 +38,7 @@ This can be done by setting the parameter ``n_vp`` in the ``run_simulation.py``
 Contributions to this PyNEST model implementation
 -------------------------------------------------
 
-2023: inital bersion of code and documentation by Felix J. Schmitt, Vahid Rostami and Martin Nawrot.
+2023: initial version of code and documentation by Felix J. Schmitt, Vahid Rostami and Martin Nawrot.
 
 Acknowledgments
 ---------------

From 998daf67b613cf129b1bf12c2700d91282ae2c8e Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Fri, 15 Mar 2024 13:08:31 +0100
Subject: [PATCH 17/23] Limit line length to pep8 in comments

---
 .../examples/EI_clustered_network/helper.py   | 41 ++++++---
 .../examples/EI_clustered_network/network.py  | 92 ++++++++++++-------
 .../EI_clustered_network/network_params.py    | 16 ++--
 .../EI_clustered_network/run_simulation.py    | 10 +-
 .../EI_clustered_network/stimulus_params.py   |  2 +-
 5 files changed, 106 insertions(+), 55 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index bf9e05e608..ab78ce7cb6 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -22,7 +22,7 @@
 """PyNEST EI-clustered network: Helper Functions
 ------------------------------------------------
 
-Helper functions to calculate synaptic weights and first passage times for exponential synapses to construct
+Helper functions to calculate synaptic weights to construct
 random balanced networks and plot raster plot with color groups.
 """
 
@@ -33,7 +33,8 @@
 
 
 def postsynaptic_current_to_potential(tau_m, tau_syn, c_m=1.0, e_l=0.0):
-    """Maximum post-synaptic potential amplitude for exponential synapses and a synaptic efficacy J of 1 pA.
+    """Maximum post-synaptic potential amplitude
+    for exponential synapses and a synaptic efficacy J of 1 pA.
 
     Parameters
     ----------
@@ -51,13 +52,26 @@ def postsynaptic_current_to_potential(tau_m, tau_syn, c_m=1.0, e_l=0.0):
     float
         maximum psp amplitude (for a 1 pA current spike) [mV]
     """
-    tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)  # time of maximum deflection of the psp  [ms]
-    pre = tau_m * tau_syn / c_m / (tau_syn - tau_m)  # we assume here the current spike is 1 pA, otherwise [mV/pA]
+    # time of maximum deflection of the psp  [ms]
+    tmax = np.log(tau_syn / tau_m) / (1 / tau_m - 1 / tau_syn)
+    # we assume here the current spike is 1 pA, otherwise [mV/pA]
+    pre = tau_m * tau_syn / c_m / (tau_syn - tau_m)
     return (e_l - pre) * np.exp(-tmax / tau_m) + pre * np.exp(-tmax / tau_syn)
 
 
 def calculate_RBN_weights(params):
-    """Calculate synaptic weights for exponential synapses before clustering, for a random balanced network.
+    """Calculate synaptic weights for a random balanced network.
+
+    The synaptic weights are calculated according to the method
+    described in Eqs. 7-10 [1]_.
+
+    References
+    ----------
+    .. [1] Rostami V, Rost T, Riehle A, van Albada SJ and Nawrot MP. 2020.
+        Excitatory and inhibitory motor cortical clusters account for balance,
+        variability, and task performance. bioRxiv 2020.02.27.968339.
+        DOI: `10.1101/2020.02.27.968339
+        <https://doi.org/10.1101/2020.02.27.968339>`__.
 
     Parameters
     ----------
@@ -99,7 +113,7 @@ def calculate_RBN_weights(params):
 
 
 def rheobase_current(tau_m, e_l, v_th, c_m):
-    """Rheobase current for a given membrane time constant and resting potential.
+    """Rheobase current for membrane time constant and resting potential.
 
     Parameters
     ----------
@@ -123,19 +137,24 @@ def rheobase_current(tau_m, e_l, v_th, c_m):
 def raster_plot(spiketimes, tlim=None, colorgroups=None, ax=None, markersize=0.5):
     """Raster plot of spiketimes.
 
-    Plots raster plot of spiketimes withing given time limits and colors neurons according to colorgroups.
+    Plots raster plot of spiketimes withing given time limits and
+    colors neurons according to colorgroups.
 
     Parameters
     ----------
     spiketimes: ndarray
-        2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+        2D array [2xN_Spikes]
+        of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
     tlim: list of floats (optional)
-        Time limits of plot: [tmin, tmax], if None: [min(spiketimes), max(spiketimes)]
+        Time limits of plot: [tmin, tmax],
+        if None: [min(spiketimes), max(spiketimes)]
     colorgroups: list of tuples (optional)
-        Each element is a tuple in the format (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons in
+        Each element is a tuple in the format
+        (color, start_neuron_ID, stop_neuron_ID]) for coloring neurons in
         given range, if None is given all neurons are black.
     ax: axis object (optional)
-        If None a new figure is created, else the plot is added to the given axis.
+        If None a new figure is created,
+        else the plot is added to the given axis.
     markersize: float (optional)
         Size of markers. (default: 0.5)
 
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 905b48ae58..ac27a8a4f2 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -22,7 +22,8 @@
 """PyNEST EI-clustered network: Network Class
 ---------------------------------------------
 
-``ClusteredNetwork`` class with functions to build and simulate the EI-clustered network.
+``ClusteredNetwork`` class with functions to build and simulate
+the EI-clustered network.
 """
 
 import pickle
@@ -35,8 +36,10 @@
 class ClusteredNetwork:
     """EI-clustered network objeect to build and simulate the network.
 
-    Provides functions to create neuron populations, stimulation devices and recording devices for an
-    EI-clustered network and setups the simulation in NEST (v3.x).
+    Provides functions to create neuron populations,
+    stimulation devices and recording devices for an
+    EI-clustered network and setups the simulation in
+    NEST (v3.x).
 
     Attributes
     ----------
@@ -55,7 +58,8 @@ class ClusteredNetwork:
     def __init__(self, sim_dict, net_dict, stim_dict):
         """Initialize the ClusteredNetwork object.
 
-        Parameters are given and explained in the files network_params.py, sim_params.py and stimulus_params.py.
+        Parameters are given and explained in the files network_params.py,
+        sim_params.py and stimulus_params.py.
 
         Parameters
         ----------
@@ -67,8 +71,9 @@ def __init__(self, sim_dict, net_dict, stim_dict):
             Dictionary with stimulus parameters.
         """
         # merge dictionaries of simulation, network and stimulus parameters
-        self._params = {**sim_dict, **net_dict, **stim_dict}  # loo
-        self._populations = []  # list of neuron population groups [E_pops, I_pops]
+        self._params = {**sim_dict, **net_dict, **stim_dict}
+        # list of neuron population groups [E_pops, I_pops]
+        self._populations = []
         self._recording_devices = []
         self._currentsources = []
         self._model_build_pipeline = [
@@ -94,7 +99,8 @@ def setup_nest(self):
         """Initializes the NEST kernel.
 
         Reset the NEST kernel and pass parameters to it.
-        Updates randseed of parameters to the actual used one if none is supplied.
+        Updates randseed of parameters to the actual
+        used one if none is supplied.
         """
         nest.ResetKernel()
         nest.set_verbosity("M_WARNING")
@@ -106,7 +112,8 @@ def setup_nest(self):
     def create_populations(self):
         """Create all neuron populations.
 
-        n_clusters excitatory and inhibitory neuron populations with the parameters of the network are created.
+        n_clusters excitatory and inhibitory neuron populations
+        with the parameters of the network are created.
         """
         # make sure number of clusters and units are compatible
         if self._params["N_E"] % self._params["n_clusters"] != 0:
@@ -185,7 +192,8 @@ def create_populations(self):
         self._populations = [E_pops, I_pops]
 
     def connect(self):
-        """Connect the excitatory and inhibitory populations with each other in the EI-clustered scheme
+        """Connect the excitatory and inhibitory populations with each other
+        in the EI-clustered scheme
 
         Raises
         ------
@@ -203,10 +211,11 @@ def connect(self):
     def connect_probabilities(self):
         """Connect the clusters with a probability EI-clustered scheme
 
-        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
-        by increasing the probabilies of the connections within the clusters and decreasing the probabilites of the
-        connections between the clusters. The weights are calculated so that the total input to a neuron
-        is balanced.
+        Connects the excitatory and inhibitory populations with each other
+        in the EI-clustered scheme by increasing the probabilities of the
+        connections within the clusters and decreasing the probabilities of the
+        connections between the clusters. The weights are calculated so that
+        the total input to a neuron is balanced.
         """
         #  self._populations[0] -> Excitatory population
         #  self._populations[1] -> Inhibitory population
@@ -420,10 +429,11 @@ def connect_probabilities(self):
     def connect_weight(self):
         """Connect the clusters with a weight EI-clustered scheme
 
-        Connects the excitatory and inhibitory populations with each other in the EI-clustered scheme
-        by increasing the weights of the connections within the clusters and decreasing the weights of the
-        connections between the clusters. The weights are calculated so that the total input to a neuron
-        is balanced.
+        Connects the excitatory and inhibitory populations with
+        each other in the EI-clustered scheme by increasing the weights
+        of the connections within the clusters and decreasing the weights
+        of the connections between the clusters. The weights are calculated
+        so that the total input to a neuron is balanced.
         """
         #  self._populations[0] -> Excitatory population
         #  self._populations[1] -> Inhibitory population
@@ -628,7 +638,8 @@ def create_stimulation(self):
     def create_recording_devices(self):
         """Creates a spike recorder
 
-        Create and connect a spike recorder to all neuron populations in self._populations.
+        Create and connect a spike recorder to all neuron populations
+        in self._populations.
         """
         self._recording_devices = [nest.Create("spike_recorder")]
         self._recording_devices[0].record_to = "memory"
@@ -653,7 +664,8 @@ def set_model_build_pipeline(self, Pipeline):
     def setup_network(self):
         """Setup network in NEST
 
-        Initializes NEST and creates the network in NEST, ready to be simulated.
+        Initializes NEST and creates
+        the network in NEST, ready to be simulated.
         Functions saved in _model_build_pipeline are executed.
         """
         for func in self._model_build_pipeline:
@@ -666,14 +678,17 @@ def simulate(self):
     def get_recordings(self):
         """Extract spikes from Spikerecorder
 
-        Extract spikes form the Spikerecorder connected to all populations created in create_populations.
+        Extract spikes form the Spikerecorder connected
+        to all populations created in create_populations.
         Cuts the warmup period away and sets time relative to end of warmup.
-        Ids 1:N_E correspond to excitatory neurons, N_E+1:N_E+N_I correspond to inhibitory neurons.
+        Ids 1:N_E correspond to excitatory neurons,
+        N_E+1:N_E+N_I correspond to inhibitory neurons.
 
         Returns
         -------
         spiketimes: ndarray
-            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+            2D array [2xN_Spikes]
+            of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
         """
         events = nest.GetStatus(self._recording_devices[0], "events")[0]
         # convert them to the format accepted by spiketools
@@ -699,26 +714,33 @@ def create_and_simulate(self):
         Returns
         -------
         spiketimes: ndarray
-            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+            2D array [2xN_Spikes]
+            of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
         """
         self.setup_network()
         self.simulate()
         return self.get_recordings()
 
     def get_firing_rates(self, spiketimes=None):
-        """Calculates the firing rates of all excitatory neurons and the firing rates of all inhibitory neurons.
+        """Calculates the average firing rates of
+        all excitatory and inhibitory neurons.
+
+        Calculates the firing rates of all excitatory neurons
+        and the firing rates of all inhibitory neurons
+        created by self.create_populations.
+        If spiketimes are not supplied, they get extracted.
 
-        Calculates the firing rates of all excitatory neurons and the firing rates of all inhibitory neurons
-        created by self.create_populations. If spiketimes are not supplied, they get extracted.
         Parameters
         ----------
         spiketimes: ndarray
-            2D array [2xN_Spikes] of spiketimes with spiketimes in row 0 and neuron IDs in row 1.
+            2D array [2xN_Spikes] of spiketimes
+            with spiketimes in row 0 and neuron IDs in row 1.
 
         Returns
         -------
         tuple[float, float]
-            average firing rates of excitatory (0) and inhibitory (1) neurons (spikes/s)
+            average firing rates of excitatory (0)
+            and inhibitory (1) neurons (spikes/s)
         """
         if spiketimes is None:
             spiketimes = self.get_recordings()
@@ -730,7 +752,8 @@ def get_firing_rates(self, spiketimes=None):
 
     def set_I_x(self, I_XE, I_XI):
         """Set DC currents for excitatory and inhibitory neurons
-        Adds DC currents for the excitatory and inhibitory neurons. The DC currents are added to the currents already
+        Adds DC currents for the excitatory and inhibitory neurons.
+        The DC currents are added to the currents already
         present in the populations.
 
         Parameters
@@ -750,9 +773,11 @@ def set_I_x(self, I_XE, I_XI):
     def get_simulation(self, PathSpikes=None):
         """Create network, simulate and return results
 
-        Creates the EI-clustered network and simulates it with the parameters supplied in the object creation.
-        Returns a dictionary with firing rates, timing information (dict) and parameters (dict). If PathSpikes is
-        supplied the spikes get saved to a pickle file.
+        Creates the EI-clustered network and simulates it with
+        the parameters supplied in the object creation.
+        Returns a dictionary with firing rates,
+        timing information (dict) and parameters (dict).
+        If PathSpikes is supplied the spikes get saved to a pickle file.
 
         Parameters
         ----------
@@ -762,7 +787,8 @@ def get_simulation(self, PathSpikes=None):
         Returns
         -------
         dict
-         Dictionary with firing rates, spiketimes (ndarray) and parameters (dict)
+         Dictionary with firing rates,
+         spiketimes (ndarray) and parameters (dict)
         """
 
         self.setup_network()
diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
index 7122e13b51..0521cddc2a 100644
--- a/pynest/examples/EI_clustered_network/network_params.py
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -60,7 +60,8 @@
     "I_th_E": 1.25,
     # Feed forward inhibitory input [rheobase current]
     "I_th_I": 0.78,
-    # distribution of feed forward input, uniform around I_th with half width I_width
+    # distribution of feed forward input,
+    # I_th*[1-delta_I_../2, 1+delta_I_../2]
     "delta_I_xE": 0.0,  # excitatory
     "delta_I_xI": 0.0,  # inhibitory
     # initial membrane potential
@@ -83,15 +84,18 @@
     "gei": 1.2,  # I to E
     "gie": 1.0,  # E to I
     "gii": 1.0,  # I to I
-    # additional scaling factor for all weights - can be used to scale weights with network size
+    # additional scaling factor for all weights
+    # - can be used to scale weights with network size
     "s": 1.0,
-    # fixed indegree - if false the synapses are established with probability ps
+    # fixed indegree - otherwise established with probability ps
     "fixed_indegree": False,
-    # cluster network by weight or connection probability - "weight" or "probabilities"
+    # cluster network by "weight" or "probabilities"
     "clustering": "weight",
-    # ratio excitatory to inhibitory clustering, rj = 0 means no clustering, which resembles a clustered network
+    # ratio excitatory to inhibitory clustering,
+    # rj = 0 means no clustering, which resembles a clustered network
     # with a blanket of inhibition
     "rj": 0.82,
-    # excitatory clustering factor, rep = 1 means no clustering, reselmbles a balanced random network
+    # excitatory clustering factor,
+    # rep = 1 means no clustering, reselmbles a balanced random network
     "rep": 6.0,
 }
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 2d046400a4..7fd7bf9ba2 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -22,8 +22,8 @@
 """PyNEST EI-clustered network: Run Simulation
 -----------------------------------------------
 
-This is an example script for running the EI-clustered model with two stimulations
-and generating a raster plot.
+This is an example script for running the EI-clustered model
+with two stimulations and generating a raster plot.
 """
 
 import matplotlib.pyplot as plt
@@ -38,8 +38,10 @@
     EI_Network = network.ClusteredNetwork(sim_dict, net_dict, stim_dict)
 
     # Runs the simulation and returns the spiketimes
-    # get simulation initializes the network in NEST and runs the simulation
-    # it returns a dict with the average rates, the spiketimes and the used parameters
+    # get simulation initializes the network in NEST
+    # and runs the simulation
+    # it returns a dict with the average rates,
+    # the spiketimes and the used parameters
     Result = EI_Network.get_simulation()
     ax = raster_plot(
         Result["spiketimes"],
diff --git a/pynest/examples/EI_clustered_network/stimulus_params.py b/pynest/examples/EI_clustered_network/stimulus_params.py
index a78f430be4..f5110a11e5 100644
--- a/pynest/examples/EI_clustered_network/stimulus_params.py
+++ b/pynest/examples/EI_clustered_network/stimulus_params.py
@@ -27,7 +27,7 @@
 """
 
 stim_dict = {
-    # list of clusters to be stimulated (None for no stimulation, 0-n_clusters-1)
+    # list of clusters to be stimulated (None: no stimulation, 0-n_clusters-1)
     "stim_clusters": [2, 3, 4],
     # stimulus amplitude (in pA)
     "stim_amp": 0.15,

From 8011d4877f6d7d3a51b40cda6084c3b937615044 Mon Sep 17 00:00:00 2001
From: Felix Schmitt <felix.schmitt@pegasus-elektra.de>
Date: Mon, 8 Apr 2024 16:03:01 +0200
Subject: [PATCH 18/23] Apply suggestions from code review

Co-authored-by: Hans Ekkehard Plesser <hans.ekkehard.plesser@nmbu.no>
---
 .../examples/EI_clustered_network/README.rst  |  2 +-
 .../examples/EI_clustered_network/helper.py   | 19 +++++++++++++------
 .../EI_clustered_network/network_params.py    |  1 +
 .../EI_clustered_network/run_simulation.py    |  8 ++++----
 4 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index 63e0949f5c..cfa485fd96 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -17,7 +17,7 @@ File structure
 --------------
 
 * :doc:`run_simulation.py <run_simulation>`: an example script to try out the EI-clustered circuit model
-* :doc:`network.py <network>`: the main Network class with functions to build and simulate the network
+* :doc:`network.py <network>`: the main ``Network`` class with functions to build and simulate the network
 * :doc:`helper.py <helper>`: helper functions for calculation of synaptic weights and currents and plot function for raster plots
 * :doc:`network_params.py <network_params>`: network and neuron parameters
 * :doc:`stimulus_params.py <stimulus_params>`: parameters for optional external stimulation
diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index ab78ce7cb6..9205b02af0 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -83,24 +83,31 @@ def calculate_RBN_weights(params):
     ndarray
         synaptic weights 2x2 matrix [[EE, EI], [IE, II]]
     """
+
     N_E = params.get("N_E")  # excitatory units
     N_I = params.get("N_I")  # inhibitory units
     N = N_E + N_I  # total units
-    baseline_conn_prob = params.get("baseline_conn_prob")  # connection probs
-    gei = params.get("gei")
-    gii = params.get("gii")
-    gie = params.get("gie")
+
+    E_L = params.get("E_L")
     V_th_E = params.get("V_th_E")  # threshold voltage
     V_th_I = params.get("V_th_I")
+    
     tau_E = params.get("tau_E")
     tau_I = params.get("tau_I")
-    E_L = params.get("E_L")
+    
     tau_syn_ex = params.get("tau_syn_ex")
     tau_syn_in = params.get("tau_syn_in")
+    
+    gei = params.get("gei")
+    gii = params.get("gii")
+    gie = params.get("gie")
+    
     amp_EE = postsynaptic_current_to_potential(tau_E, tau_syn_ex)
     amp_EI = postsynaptic_current_to_potential(tau_E, tau_syn_in)
     amp_IE = postsynaptic_current_to_potential(tau_I, tau_syn_ex)
-    amp_II = postsynaptic_current_to_potential(tau_I, tau_syn_in)
+    amp_II = postsynaptic_current_to_potential(tau_I, tau_syn_in)    
+    
+    baseline_conn_prob = params.get("baseline_conn_prob")  # connection probs
 
     js = np.zeros((2, 2))
     K_EE = N_E * baseline_conn_prob[0, 0]
diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
index 0521cddc2a..1daa143ebb 100644
--- a/pynest/examples/EI_clustered_network/network_params.py
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -66,6 +66,7 @@
     "delta_I_xI": 0.0,  # inhibitory
     # initial membrane potential
     "V_m": "rand",
+
     ############################################
     # network parameters
     ############################################
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 7fd7bf9ba2..81568c7b5f 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -35,14 +35,14 @@
 
 if __name__ == "__main__":
     # Creates object which creates the EI clustered network in NEST
-    EI_Network = network.ClusteredNetwork(sim_dict, net_dict, stim_dict)
+    ei_network = network.ClusteredNetwork(sim_dict, net_dict, stim_dict)
 
     # Runs the simulation and returns the spiketimes
     # get simulation initializes the network in NEST
     # and runs the simulation
     # it returns a dict with the average rates,
     # the spiketimes and the used parameters
-    Result = EI_Network.get_simulation()
+    result = ei_network.get_simulation()
     ax = raster_plot(
         Result["spiketimes"],
         tlim=(0, sim_dict["simtime"]),
@@ -52,5 +52,5 @@
         ],
     )
     plt.savefig("clustered_ei_raster.png")
-    print("Firing rate of excitatory neurons: " + str(Result["e_rate"]) + " Spikes/s")
-    print("Firing rate of inhibitory neurons: " + str(Result["i_rate"]) + " Spikes/s")
+    print(f"Firing rate of excitatory neurons: {result['e_rate']:6.2f} spikes/s")
+    print(f"Firing rate of inhibitory neurons: {result['i_rate']:6.2f} spikes/s")

From c340d594ee933142cd1807b9fe15de8a2387f497 Mon Sep 17 00:00:00 2001
From: Felix Schmitt <felix.schmitt@pegasus-elektra.de>
Date: Mon, 8 Apr 2024 16:08:01 +0200
Subject: [PATCH 19/23] Apply suggestions from code review

Co-authored-by: Hans Ekkehard Plesser <hans.ekkehard.plesser@nmbu.no>
---
 doc/htmldoc/examples/index.rst                  | 2 +-
 pynest/examples/EI_clustered_network/network.py | 9 ++++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst
index 7b8a0fb6aa..4ae21f495d 100644
--- a/doc/htmldoc/examples/index.rst
+++ b/doc/htmldoc/examples/index.rst
@@ -82,7 +82,7 @@ PyNEST examples
 
 
 
-    .. grid-item-card:: EI clustered network (Rostami)
+    .. grid-item-card:: EI clustered network (Rostami et al)
            :img-top: ../static/img/pynest/EI_clustered_network_schematic.png
 
            :doc:`../auto_examples/EI_clustered_network/index`
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index ac27a8a4f2..0449df488c 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -70,6 +70,7 @@ def __init__(self, sim_dict, net_dict, stim_dict):
         stim_dict: dict
             Dictionary with stimulus parameters.
         """
+
         # merge dictionaries of simulation, network and stimulus parameters
         self._params = {**sim_dict, **net_dict, **stim_dict}
         # list of neuron population groups [E_pops, I_pops]
@@ -102,6 +103,7 @@ def setup_nest(self):
         Updates randseed of parameters to the actual
         used one if none is supplied.
         """
+
         nest.ResetKernel()
         nest.set_verbosity("M_WARNING")
         nest.local_num_threads = self._params.get("n_vp", 4)
@@ -115,6 +117,7 @@ def create_populations(self):
         n_clusters excitatory and inhibitory neuron populations
         with the parameters of the network are created.
         """
+
         # make sure number of clusters and units are compatible
         if self._params["N_E"] % self._params["n_clusters"] != 0:
             raise ValueError("N_E must be a multiple of Q")
@@ -651,15 +654,15 @@ def create_recording_devices(self):
             all_units += I_pop
         nest.Connect(all_units, self._recording_devices[0], "all_to_all")  # Spikerecorder
 
-    def set_model_build_pipeline(self, Pipeline):
+    def set_model_build_pipeline(self, pipeline):
         """Set _model_build_pipeline
 
         Parameters
         ----------
-        Pipeline: list
+        pipeline: list
             ordered list of functions executed to build the network model
         """
-        self._model_build_pipeline = Pipeline
+        self._model_build_pipeline = pipeline
 
     def setup_network(self):
         """Setup network in NEST

From 6a0c95c2c10ae0b43b0ce7191e2affb50dc4adab Mon Sep 17 00:00:00 2001
From: Felix Schmitt <felix.schmitt@pegasus-elektra.de>
Date: Mon, 8 Apr 2024 16:20:18 +0200
Subject: [PATCH 20/23] Apply suggestions from code review

Co-authored-by: Hans Ekkehard Plesser <hans.ekkehard.plesser@nmbu.no>
---
 pynest/examples/EI_clustered_network/network_params.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
index 1daa143ebb..1ee398c5e1 100644
--- a/pynest/examples/EI_clustered_network/network_params.py
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -64,7 +64,7 @@
     # I_th*[1-delta_I_../2, 1+delta_I_../2]
     "delta_I_xE": 0.0,  # excitatory
     "delta_I_xI": 0.0,  # inhibitory
-    # initial membrane potential
+    # initial membrane potential: either a float (in mV) to initialize all neurons to a fixed value or "rand" for randomized values
     "V_m": "rand",
 
     ############################################

From dbe76ed3efbc850f8a97950dbedd5433f94580fc Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Mon, 8 Apr 2024 16:56:06 +0200
Subject: [PATCH 21/23] Requested changes from code review

---
 .../examples/EI_clustered_network/helper.py   | 14 +++--
 .../examples/EI_clustered_network/network.py  | 52 ++++++++++++-------
 .../EI_clustered_network/network_params.py    |  4 +-
 .../EI_clustered_network/run_simulation.py    |  2 +-
 4 files changed, 42 insertions(+), 30 deletions(-)

diff --git a/pynest/examples/EI_clustered_network/helper.py b/pynest/examples/EI_clustered_network/helper.py
index 9205b02af0..902535849e 100644
--- a/pynest/examples/EI_clustered_network/helper.py
+++ b/pynest/examples/EI_clustered_network/helper.py
@@ -29,8 +29,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-small = 1e-10
-
 
 def postsynaptic_current_to_potential(tau_m, tau_syn, c_m=1.0, e_l=0.0):
     """Maximum post-synaptic potential amplitude
@@ -91,22 +89,22 @@ def calculate_RBN_weights(params):
     E_L = params.get("E_L")
     V_th_E = params.get("V_th_E")  # threshold voltage
     V_th_I = params.get("V_th_I")
-    
+
     tau_E = params.get("tau_E")
     tau_I = params.get("tau_I")
-    
+
     tau_syn_ex = params.get("tau_syn_ex")
     tau_syn_in = params.get("tau_syn_in")
-    
+
     gei = params.get("gei")
     gii = params.get("gii")
     gie = params.get("gie")
-    
+
     amp_EE = postsynaptic_current_to_potential(tau_E, tau_syn_ex)
     amp_EI = postsynaptic_current_to_potential(tau_E, tau_syn_in)
     amp_IE = postsynaptic_current_to_potential(tau_I, tau_syn_ex)
-    amp_II = postsynaptic_current_to_potential(tau_I, tau_syn_in)    
-    
+    amp_II = postsynaptic_current_to_potential(tau_I, tau_syn_in)
+
     baseline_conn_prob = params.get("baseline_conn_prob")  # connection probs
 
     js = np.zeros((2, 2))
diff --git a/pynest/examples/EI_clustered_network/network.py b/pynest/examples/EI_clustered_network/network.py
index 0449df488c..306014db17 100644
--- a/pynest/examples/EI_clustered_network/network.py
+++ b/pynest/examples/EI_clustered_network/network.py
@@ -173,7 +173,8 @@ def create_populations(self):
             else self._params["V_th_I"] - 20 * nest.random.lognormal(0, 1),
         }
 
-        # iaf_psc_exp allows stochasticity, if not used - ignore
+        # iaf_psc_exp allows stochasticity, if not used - don't supply the parameters and use
+        # iaf_psc_exp as deterministic model
         if (self._params.get("delta") is not None) and (self._params.get("rho") is not None):
             E_neuron_params["delta"] = self._params["delta"]
             I_neuron_params["delta"] = self._params["delta"]
@@ -212,7 +213,7 @@ def connect(self):
             raise ValueError("Clustering method %s not implemented" % self._params["clustering"])
 
     def connect_probabilities(self):
-        """Connect the clusters with a probability EI-clustered scheme
+        """Connect the clusters with a probability EI-cluster scheme
 
         Connects the excitatory and inhibitory populations with each other
         in the EI-clustered scheme by increasing the probabilities of the
@@ -220,12 +221,14 @@ def connect_probabilities(self):
         connections between the clusters. The weights are calculated so that
         the total input to a neuron is balanced.
         """
+
         #  self._populations[0] -> Excitatory population
         #  self._populations[1] -> Inhibitory population
-        # connectivity parameters
-        js = self._params["js"]  # connection weights
+
         N = self._params["N_E"] + self._params["N_I"]  # total units
-        if np.isnan(js).any():
+        # if js are not given compute them so that sqrt(K) spikes equal v_thr-E_L and rows are balanced
+        # if any of the js is nan or not given
+        if self._params.get("js") is None or np.isnan(self._params.get("js")).any():
             js = helper.calculate_RBN_weights(self._params)
         js *= self._params["s"]
 
@@ -238,22 +241,30 @@ def connect_probabilities(self):
         p_plus = self._params["pplus"] * self._params["baseline_conn_prob"]
         p_minus = pminus * self._params["baseline_conn_prob"]
 
+        # Connection probabilities within clusters can exceed 1. In this case, we iteratively split
+        # the connections in multiple synapse populations with probabilities < 1.
         iterations = np.ones((2, 2), dtype=int)
         # test if any of the probabilities is larger than 1
         if np.any(p_plus > 1):
-            print("The probability of connection is larger than 1")
-            print("p_plus: ", p_plus)
-            print("p_minus: ", p_minus)
+            print("The probability of some connections is larger than 1.")
+            print("Pre-splitting the connections in multiple synapse populations:")
+            printoptions = np.get_printoptions()
+            np.set_printoptions(precision=2, floatmode="fixed")
+            print("p_plus:\n", p_plus)
+            print("p_minus:\n", p_minus)
             for i in range(2):
                 for j in range(2):
                     if p_plus[i, j] > 1:
                         iterations[i, j] = int(np.ceil(p_plus[i, j]))
                         p_plus[i, j] /= iterations[i, j]
-            print("p_plus: ", p_plus)
-            print(iterations)
+            print("\nPost-splitting the connections in multiple synapse populations:")
+            print("p_plus:\n", p_plus)
+            print("Number of synapse populations:\n", iterations)
+            np.set_printoptions(**printoptions)
 
         # define the synapses and connect the populations
-        # EE
+
+        # Excitatory to excitatory neuron connections
         j_ee = js[0, 0] / np.sqrt(N)
         nest.CopyModel("static_synapse", "EE", {"weight": j_ee, "delay": self._params["delay"]})
 
@@ -297,7 +308,7 @@ def connect_probabilities(self):
                 else:
                     nest.Connect(pre, post, conn_params_EE_minus, "EE")
 
-        # EI
+        # Inhibitory to excitatory neuron connections
         j_ei = js[0, 1] / np.sqrt(N)
         nest.CopyModel("static_synapse", "EI", {"weight": j_ei, "delay": self._params["delay"]})
 
@@ -341,7 +352,7 @@ def connect_probabilities(self):
                 else:
                     nest.Connect(pre, post, conn_params_EI_minus, "EI")
 
-        # IE
+        # Excitatory to inhibitory neuron connections
         j_ie = js[1, 0] / np.sqrt(N)
         nest.CopyModel("static_synapse", "IE", {"weight": j_ie, "delay": self._params["delay"]})
 
@@ -385,7 +396,7 @@ def connect_probabilities(self):
                 else:
                     nest.Connect(pre, post, conn_params_IE_minus, "IE")
 
-        # II
+        # Inhibitory to inhibitory neuron connections
         j_ii = js[1, 1] / np.sqrt(N)
         nest.CopyModel("static_synapse", "II", {"weight": j_ii, "delay": self._params["delay"]})
 
@@ -430,7 +441,7 @@ def connect_probabilities(self):
                     nest.Connect(pre, post, conn_params_II_minus, "II")
 
     def connect_weight(self):
-        """Connect the clusters with a weight EI-clustered scheme
+        """Connect the clusters with a weight EI-cluster scheme
 
         Connects the excitatory and inhibitory populations with
         each other in the EI-clustered scheme by increasing the weights
@@ -438,6 +449,7 @@ def connect_weight(self):
         of the connections between the clusters. The weights are calculated
         so that the total input to a neuron is balanced.
         """
+
         #  self._populations[0] -> Excitatory population
         #  self._populations[1] -> Inhibitory population
 
@@ -457,7 +469,8 @@ def connect_weight(self):
             jminus = np.ones((2, 2))
 
         # define the synapses and connect the populations
-        # EE
+
+        # Excitatory to excitatory neuron connections
         j_ee = js[0, 0] / np.sqrt(N)
         nest.CopyModel(
             "static_synapse",
@@ -497,7 +510,7 @@ def connect_weight(self):
                 else:
                     nest.Connect(pre, post, conn_params_EE, "EE_minus")
 
-        # EI
+        # Inhibitory to excitatory neuron connections
         j_ei = js[0, 1] / np.sqrt(N)
         nest.CopyModel(
             "static_synapse",
@@ -535,7 +548,8 @@ def connect_weight(self):
                     nest.Connect(pre, post, conn_params_EI, "EI_plus")
                 else:
                     nest.Connect(pre, post, conn_params_EI, "EI_minus")
-        # IE
+
+        # Excitatory to inhibitory neuron connections
         j_ie = js[1, 0] / np.sqrt(N)
         nest.CopyModel(
             "static_synapse",
@@ -575,7 +589,7 @@ def connect_weight(self):
                 else:
                     nest.Connect(pre, post, conn_params_IE, "IE_minus")
 
-        # II
+        # Inhibitory to inhibitory neuron connections
         j_ii = js[1, 1] / np.sqrt(N)
         nest.CopyModel(
             "static_synapse",
diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
index 1ee398c5e1..0612c2bb0c 100644
--- a/pynest/examples/EI_clustered_network/network_params.py
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -64,9 +64,9 @@
     # I_th*[1-delta_I_../2, 1+delta_I_../2]
     "delta_I_xE": 0.0,  # excitatory
     "delta_I_xI": 0.0,  # inhibitory
-    # initial membrane potential: either a float (in mV) to initialize all neurons to a fixed value or "rand" for randomized values
+    # initial membrane potential: either a float (in mV) to initialize all neurons to a fixed value
+    # or "rand" for randomized values: "V_th_{E,I}" - 20 * nest.random.lognormal(0, 1)
     "V_m": "rand",
-
     ############################################
     # network parameters
     ############################################
diff --git a/pynest/examples/EI_clustered_network/run_simulation.py b/pynest/examples/EI_clustered_network/run_simulation.py
index 81568c7b5f..ecafa112df 100644
--- a/pynest/examples/EI_clustered_network/run_simulation.py
+++ b/pynest/examples/EI_clustered_network/run_simulation.py
@@ -44,7 +44,7 @@
     # the spiketimes and the used parameters
     result = ei_network.get_simulation()
     ax = raster_plot(
-        Result["spiketimes"],
+        result["spiketimes"],
         tlim=(0, sim_dict["simtime"]),
         colorgroups=[
             ("k", 0, net_dict["N_E"]),

From 34b9ce03e659d719de22445230a8762cba4facf7 Mon Sep 17 00:00:00 2001
From: Felix Schmitt <felix.schmitt@pegasus-elektra.de>
Date: Tue, 9 Apr 2024 10:27:03 +0200
Subject: [PATCH 22/23] Apply suggestions from code review

Co-authored-by: jessica-mitchell <mitchell20j@gmail.com>
---
 pynest/examples/EI_clustered_network/README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/examples/EI_clustered_network/README.rst b/pynest/examples/EI_clustered_network/README.rst
index cfa485fd96..4f7d5a43d7 100644
--- a/pynest/examples/EI_clustered_network/README.rst
+++ b/pynest/examples/EI_clustered_network/README.rst
@@ -6,7 +6,7 @@ This is PyNEST implementation of the EI-clustered circuit model described by Ros
 .. figure:: /static/img/pynest/EI_clustered_network_schematic.png
    :alt: EI-clustered circuit model.
 
-   Schematic of the EI-clustered circuit model. The network consists of n_clusters clusters with one excitatory and one inhibitory population each.
+   Schematic of the EI-clustered circuit model. The network consists of `n_clusters` with one excitatory and one inhibitory population each.
 
 Citing this code
 ----------------

From 196784f9756472f14a81628726b4a18308165202 Mon Sep 17 00:00:00 2001
From: schmitfe <felix.schmitt@uni-koeln.de>
Date: Tue, 9 Apr 2024 10:38:00 +0200
Subject: [PATCH 23/23] Corrected copyright header

---
 pynest/examples/EI_clustered_network/network_params.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/examples/EI_clustered_network/network_params.py b/pynest/examples/EI_clustered_network/network_params.py
index 0612c2bb0c..c40eb9cb48 100644
--- a/pynest/examples/EI_clustered_network/network_params.py
+++ b/pynest/examples/EI_clustered_network/network_params.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# sim_params.py
+# network_params.py
 #
 # This file is part of NEST.
 #