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

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

.. _sphx_glr_auto_examples_gap_junctions_two_neurons.py:


Gap Junctions: Two neuron example
---------------------------------

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----

 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%2Fgap_junctions_two_neurons.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 simulates two Hodgkin-Huxley neurons of type ``hh_psc_alpha_gap``
connected by a gap junction. Both neurons receive a constant current of
100.0 pA. The neurons are initialized with different membrane potentials and
synchronize over time due to the gap-junction connection.

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.. code-block:: Python


    import matplotlib.pyplot as plt
    import nest
    import numpy

    nest.ResetKernel()


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First we set the resolution of the simulation, create two neurons and
create a ``voltmeter`` for recording.

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.. code-block:: Python


    nest.resolution = 0.05

    neuron = nest.Create("hh_psc_alpha_gap", 2)

    vm = nest.Create("voltmeter", params={"interval": 0.1})


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Then we set the constant current input, modify the inital membrane
potential of one of the neurons and connect the neurons to the ``voltmeter``.

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.. code-block:: Python


    neuron.I_e = 100.0
    neuron[0].V_m = -10.0

    nest.Connect(vm, neuron, "all_to_all")


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In order to create the ``gap_junction`` connection we employ the
``all_to_all`` connection rule: Gap junctions are bidirectional connections,
therefore we need to connect `neuron[0]` to `neuron[1]` and `neuron[1]` to
`neuron[0]`:

.. GENERATED FROM PYTHON SOURCE LINES 63-68

.. code-block:: Python


    nest.Connect(
        neuron, neuron, {"rule": "all_to_all", "allow_autapses": False}, {"synapse_model": "gap_junction", "weight": 0.5}
    )


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Finally we start the simulation and plot the membrane potentials of both
neurons.

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.. code-block:: Python


    nest.Simulate(351.0)

    senders = vm.events["senders"]
    times = vm.events["times"]
    v_m_values = vm.events["V_m"]

    plt.figure(1)
    plt.plot(times[numpy.where(senders == 1)], v_m_values[numpy.where(senders == 1)], "r-")
    plt.plot(times[numpy.where(senders == 2)], v_m_values[numpy.where(senders == 2)], "g-")
    plt.xlabel("time (ms)")
    plt.ylabel("membrane potential (mV)")
    plt.show()


.. _sphx_glr_download_auto_examples_gap_junctions_two_neurons.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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