.. 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

        Click :ref:`here <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)
----------------------------------------------------------------------------------------------

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:: default



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

    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 50-55

.. code-block:: default


    res = 0.1
    nest.SetKernelStatus({"resolution": res})
    neuron = nest.Create("aeif_cond_alpha")


.. GENERATED FROM PYTHON SOURCE LINES 56-58

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

.. GENERATED FROM PYTHON SOURCE LINES 58-62

.. code-block:: default



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


.. GENERATED FROM PYTHON SOURCE LINES 63-65

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

.. GENERATED FROM PYTHON SOURCE LINES 65-69

.. code-block:: default


    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 70-71

We connect the DC generators.

.. GENERATED FROM PYTHON SOURCE LINES 71-74

.. code-block:: default


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


.. GENERATED FROM PYTHON SOURCE LINES 75-77

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

.. GENERATED FROM PYTHON SOURCE LINES 77-81

.. code-block:: default


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


.. GENERATED FROM PYTHON SOURCE LINES 82-84

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

.. GENERATED FROM PYTHON SOURCE LINES 84-90

.. code-block:: default


    nest.Simulate(1000.0)

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


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_brette_gerstner_fig_2c.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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