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

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

.. _sphx_glr_auto_examples_correlospinmatrix_detector_two_neuron.py:


Correlospinmatrix detector example
----------------------------------

.. 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%2Fcorrelospinmatrix_detector_two_neuron.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 scripts simulates two connected binary neurons, similar
as in [1]_. It measures and plots the auto- and cross covariance functions
of the individual neurons and between them, respectively.

References
~~~~~~~~~~

.. [1] Ginzburg and Sompolinsky (1994). Theory of correlations in stochastic neural networks. 50(4) p. 3175. Fig. 1.

.. GENERATED FROM PYTHON SOURCE LINES 36-89

.. code-block:: Python


    import matplotlib.pyplot as plt
    import nest
    import numpy as np

    m_x = 0.5
    tau_m = 10.0
    h = 0.1
    T = 1000000.0
    tau_max = 100.0

    csd = nest.Create("correlospinmatrix_detector")
    csd.set(N_channels=2, tau_max=tau_max, Tstart=tau_max, delta_tau=h)

    n1 = nest.Create("ginzburg_neuron")
    n1.set(theta=0.0, tau_m=tau_m, c_1=0.0, c_2=2.0 * m_x, c_3=1.0)

    n2 = nest.Create("mcculloch_pitts_neuron")
    n2.set(theta=0.5, tau_m=tau_m)

    nest.Connect(n1, n2, syn_spec={"weight": 1.0})

    nest.Connect(n1, csd, syn_spec={"receptor_type": 0})
    nest.Connect(n2, csd, syn_spec={"receptor_type": 1})

    nest.Simulate(T)

    count_covariance = csd.count_covariance

    mean_activities = np.zeros(2, dtype=float)
    for i in range(2):
        mean_activities[i] = count_covariance[i][i][int(tau_max / h)] * (h / T)

    print("mean activities =", mean_activities)

    covariance_matrix = np.zeros((2, 2, int(2 * tau_max / h) + 1), dtype=float)
    for i in range(2):
        for j in range(2):
            covariance_matrix[i, j] = count_covariance[i][j] * (h / T) - mean_activities[i] * mean_activities[j]

    ts = np.arange(-tau_max, tau_max + h, h)

    plt.title("auto- and cross covariance functions")

    plt.plot(ts, covariance_matrix[0, 1], "r", label=r"$c_{12}$")
    plt.plot(ts, covariance_matrix[1, 0], "b", label=r"$c_{21}$")
    plt.plot(ts, covariance_matrix[0, 0], "g", label=r"$c_{11}$")
    plt.plot(ts, covariance_matrix[1, 1], "y", label=r"$c_{22}$")
    plt.xlabel(r"time $t \; \mathrm{ms}$")
    plt.ylabel(r"$c$")
    plt.legend()

    plt.show()


.. _sphx_glr_download_auto_examples_correlospinmatrix_detector_two_neuron.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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