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

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

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

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

.. _sphx_glr_auto_examples_vinit_example.py:


Initial membrane voltage
------------------------

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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%2Fvinit_example.ipynb&branch=main
    :link-alt: JupyterHub service

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


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   :padding: 0 0 2 0

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     :class: sd-text-muted
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     :columns: 4

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

----


Plot several runs of the ``iaf_cond_exp_sfa_rr`` neuron without input for various
initial values of the membrane potential.

References
~~~~~~~~~~

.. [1] Dayan, P. and Abbott, L.F. (2001) Theoretical neuroscience,
       MIT Press, page 166

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First, the necessary modules for simulation and plotting are imported.

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.. code-block:: Python


    import matplotlib.pyplot as plt
    import nest
    import numpy


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A loop runs over a range of initial membrane voltages.

In the beginning of each iteration, the simulation kernel is put back to
its initial state using `ResetKernel`.

Next, a neuron is instantiated with ``Create``. The used neuron model
``iaf_cond_exp_sfa_rr`` is an implementation of a spiking neuron with
integrate-and-fire dynamics, conductance-based synapses, an additional
spike-frequency adaptation and relative refractory mechanisms as described
in [1]_. Incoming spike events induce a postsynaptic change of
conductance  modeled  by an  exponential  function. ``SetStatus`` allows to
assign the initial membrane voltage of the current loop run to the neuron.

``Create`` is used once more to instantiate a ``voltmeter`` as recording device
which is subsequently connected to the neuron with ``Connect``.

Then, a simulation with a duration of 75 ms is started with ``Simulate``.

When the simulation has finished, the recorded times and membrane voltages
are read from the voltmeter via ``get``.

Finally, the time course of the membrane voltages is plotted for each of
the different initial values.

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.. code-block:: Python


    for vinit in numpy.arange(-100, -50, 10, float):
        nest.ResetKernel()

        cbn = nest.Create("iaf_cond_exp_sfa_rr")
        cbn.V_m = vinit

        voltmeter = nest.Create("voltmeter")
        nest.Connect(voltmeter, cbn)

        nest.Simulate(75.0)

        t = voltmeter.get("events", "times")
        v = voltmeter.get("events", "V_m")

        plt.plot(t, v, label="initial V_m = %.2f mV" % vinit)


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Set the legend and the labels for the plot outside of the loop.

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.. code-block:: Python


    plt.legend(loc=4)
    plt.xlabel("time (ms)")
    plt.ylabel("V_m (mV)")
    plt.show()


.. _sphx_glr_download_auto_examples_vinit_example.py:

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

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

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

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

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


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

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