.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/brette_et_al_2007/coba.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_brette_et_al_2007_coba.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_brette_et_al_2007_coba.py:


Benchmark 1 of the simulator review (COBA)
------------------------------------------

.. 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%2Fbrette_et_al_2007%2Fcoba.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.

----


This script creates a sparsely coupled network of excitatory and inhibitory
neurons which exhibits self-sustained activity after an initial stimulus.
Connections within and across both populations are created at random. Both
neuron populations receive Poissonian background input. The spike output of
500 neurons from each population are recorded. Neurons are modeled as leaky
integrate-and-fire neurons with conductance-based synapses (exponential
functions). The model is based on the Vogels & Abbott network model [1]_.

This is Benchmark 1 of the FACETS simulator review (Brette et al., 2007) [2]_:
- Neuron model: integrate-and-fire (``iaf_cond_exp``)
- Synapse model: conductance-based (COBA)
- Synapse time course: exponential
- Spike times: grid-constrained

References
~~~~~~~~~~

.. [1] Vogels TP, Abbott LF. 2005. Signal propagation and logic gating in
       networks of integrate-and-fire neurons. Journal of Neuroscience.
       25(46):10786-10795.
       https://doi.org/10.1523/JNEUROSCI.3508-05.2005

.. [2] Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, et al.
       2007. Simulation of networks of spiking neurons: a review of tools and
       strategies. Journal of Computational Neuroscience. 23(3):349-398.
       https://doi.org/10.1007/s10827-007-0038-6

.. GENERATED FROM PYTHON SOURCE LINES 54-58

.. code-block:: Python


    import nest
    from brette_et_al_2007_benchmark import run_simulation


.. GENERATED FROM PYTHON SOURCE LINES 59-60

Set benchmark parameters

.. GENERATED FROM PYTHON SOURCE LINES 60-104

.. code-block:: Python


    params = {
        "model": "iaf_cond_exp",  # neuron model
        "model_params": {
            "E_L": -60.0,  # resting potential [mV]
            "V_th": -50.0,  # threshold [mV]
            "V_reset": -60.0,  # reset potential [mV]
            "t_ref": 5.0,  # refractory period [ms]
            "E_ex": 0.0,  # excitatory reversal potential [mV]
            "E_in": -80.0,  # inhibitory reversal potential [mV]
            "C_m": 200.0,  # capacity [pF]
            "g_L": 10.0,  # leak conductance [nS]
            "tau_syn_ex": 5.0,  # excitatory synaptic time constant [ms]
            "tau_syn_in": 10.0,  # inhibitory synapse time constant [ms]
        },
        "delay": 0.1,  # synaptic delay [ms]
        "E_synapse_params": {
            "weight": 6.0,  # excitatory synaptic conductance [nS]
        },
        "I_synapse_params": {
            "weight": -67.0,  # inhibitory synaptic conductance [nS]
        },
        "stimulus": "poisson_generator",
        "stimulus_params": {
            "rate": 300.0,  # rate of initial Poisson stimulus [spikes/s]
            "start": 1.0,  # start of Poisson generator [ms]
            "stop": 51.0,  # stop of Poisson generator [ms]
            "origin": 0.0,  # origin of time [ms]
        },
        "recorder": "spike_recorder",
        "recorder_params": {
            "record_to": "ascii",
            "label": "coba",
        },
        "Nrec": 500,  # number of neurons per population to record from
        "Nstim": 50,  # number of neurons to stimulate
        "simtime": 1000.0,  # simulated time [ms]
        "dt": 0.1,  # simulation step [ms]
        "NE": 3200,  # number of excitatory neurons
        "NI": 800,  # number of inhibitory neurons
        "epsilon": 0.02,  # connection probability
        "virtual_processes": 1,  # number of virtual processes to use
    }


.. GENERATED FROM PYTHON SOURCE LINES 105-106

Run the simulation

.. GENERATED FROM PYTHON SOURCE LINES 106-110

.. code-block:: Python


    if __name__ == "__main__":
        nest.set_verbosity("M_WARNING")
        results = run_simulation(params)


.. _sphx_glr_download_auto_examples_brette_et_al_2007_coba.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: coba.zip <coba.zip>`


.. only:: html

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

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