.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/brette_gerstner_fig_2c.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_gerstner_fig_2c.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_brette_gerstner_fig_2c.py:


Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 2C)
----------------------------------------------------------------------------------------------

.. 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_gerstner_fig_2c.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 example tests the adaptive integrate and fire model (AdEx) according to
Brette and Gerstner [1]_ reproduces Figure 2C of the paper.
Note that Brette and Gerstner give the value for `b` in `nA`.
To be consistent with the other parameters in the equations, `b` must be
converted to `pA` (pico Ampere).

References
~~~~~~~~~~

.. [1] Brette R and Gerstner W (2005). Adaptive exponential integrate-and-fire model as an effective
       description of neuronal activity J. Neurophysiology. https://doi.org/10.1152/jn.00686.2005

.. GENERATED FROM PYTHON SOURCE LINES 39-47

.. code-block:: Python



    import matplotlib.pyplot as plt
    import nest
    import nest.voltage_trace

    nest.ResetKernel()


.. GENERATED FROM PYTHON SOURCE LINES 48-50

First we make sure that the resolution of the simulation is 0.1 ms. This is
important, since the slop of the action potential is very steep.

.. GENERATED FROM PYTHON SOURCE LINES 50-54

.. code-block:: Python


    nest.resolution = 0.1
    neuron = nest.Create("aeif_cond_alpha")


.. GENERATED FROM PYTHON SOURCE LINES 55-57

`a` and `b` are parameters of the adex model. Their values come from the
publication.

.. GENERATED FROM PYTHON SOURCE LINES 57-60

.. code-block:: Python


    neuron.set(a=4.0, b=80.5)


.. GENERATED FROM PYTHON SOURCE LINES 61-63

Next we define the stimulus protocol. There are two DC generators,
producing stimulus currents during two time-intervals.

.. GENERATED FROM PYTHON SOURCE LINES 63-67

.. code-block:: Python


    dc = nest.Create("dc_generator", 2)
    dc.set(amplitude=[500.0, 800.0], start=[0.0, 500.0], stop=[200.0, 1000.0])


.. GENERATED FROM PYTHON SOURCE LINES 68-69

We connect the DC generators.

.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: Python


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


.. GENERATED FROM PYTHON SOURCE LINES 73-75

And add a ``voltmeter`` to sample the membrane potentials from the neuron
in intervals of 0.1 ms.

.. GENERATED FROM PYTHON SOURCE LINES 75-79

.. code-block:: Python


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


.. GENERATED FROM PYTHON SOURCE LINES 80-82

Finally, we simulate for 1000 ms and plot a voltage trace to produce the
figure.

.. GENERATED FROM PYTHON SOURCE LINES 82-88

.. code-block:: Python


    nest.Simulate(1000.0)

    nest.voltage_trace.from_device(voltmeter)
    plt.axis([0, 1000, -80, -20])
    plt.show()


.. _sphx_glr_download_auto_examples_brette_gerstner_fig_2c.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: brette_gerstner_fig_2c.ipynb <brette_gerstner_fig_2c.ipynb>`

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

      :download:`Download Python source code: brette_gerstner_fig_2c.py <brette_gerstner_fig_2c.py>`


.. only:: html

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

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