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

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

.. _sphx_glr_auto_examples_hh_psc_alpha.py:


Example using Hodgkin-Huxley neuron
-----------------------------------

.. 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%2Fhh_psc_alpha.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 produces a rate-response (FI) curve of the Hodgkin-Huxley
neuron ``hh_psc_alpha`` in response to a range of different current (DC) stimulations.
The result is plotted using matplotlib.

Since a DC input affects only the neuron's channel dynamics, this routine
does not yet check correctness of synaptic response.

.. GENERATED FROM PYTHON SOURCE LINES 33-74

.. code-block:: Python


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

    nest.set_verbosity("M_WARNING")
    nest.ResetKernel()

    simtime = 1000

    # Amplitude range, in pA
    dcfrom = 0
    dcstep = 20
    dcto = 2000

    h = 0.1  # simulation step size in mS

    neuron = nest.Create("hh_psc_alpha")
    sr = nest.Create("spike_recorder")

    sr.record_to = "memory"

    nest.Connect(neuron, sr, syn_spec={"weight": 1.0, "delay": h})

    # Simulation loop
    n_data = int(dcto / float(dcstep))
    amplitudes = np.zeros(n_data)
    event_freqs = np.zeros(n_data)
    for i, amp in enumerate(range(dcfrom, dcto, dcstep)):
        neuron.I_e = float(amp)
        print(f"Simulating with current I={amp} pA")
        nest.Simulate(1000)  # one second warm-up time for equilibrium state
        sr.n_events = 0  # then reset spike counts
        nest.Simulate(simtime)  # another simulation call to record firing rate

        n_events = sr.n_events
        amplitudes[i] = amp
        event_freqs[i] = n_events / (simtime / 1000.0)

    plt.plot(amplitudes, event_freqs)
    plt.show()


.. _sphx_glr_download_auto_examples_hh_psc_alpha.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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