.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/EI_clustered_network/network_params.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_EI_clustered_network_network_params.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_EI_clustered_network_network_params.py:

PyNEST EI-clustered network: Network Parameters
------------------------------------------------

.. only:: html

----

 Run this example as a Jupyter notebook:

  .. card::
    :width: 25%
    :margin: 2
    :text-align: center
    :link: https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fnest%2Fnest-simulator-examples&urlpath=lab%2Ftree%2Fnest-simulator-examples%2Fnotebooks%2Fnotebooks%2FEI_clustered_network%2Fnetwork_params.ipynb&branch=main
    :link-alt: JupyterHub service

    .. image:: https://nest-simulator.org/TryItOnEBRAINS.png


.. grid:: 1 1 1 1
   :padding: 0 0 2 0

   .. grid-item::
     :class: sd-text-muted
     :margin: 0 0 3 0
     :padding: 0 0 3 0
     :columns: 4

     See :ref:`our guide <run_jupyter>` for more information and troubleshooting.

----


A dictionary with parameters defining the network and neuron parameters.

.. GENERATED FROM PYTHON SOURCE LINES 28-103

.. code-block:: Python


    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,
        # 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: "V_th_{E,I}" - 20 * nest.random.lognormal(0, 1)
        "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 - otherwise established with probability ps
        "fixed_indegree": False,
        # cluster network by "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,
    }


.. _sphx_glr_download_auto_examples_EI_clustered_network_network_params.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: network_params.ipynb <network_params.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: network_params.py <network_params.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_