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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_twoneurons.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_twoneurons.py:


Two neuron example
------------------

This script simulates two connected pre- and postsynaptic neurons.
The presynaptic neuron receives a constant external current,
and the membrane potential of both neurons are recorded.

See Also
~~~~~~~~

:doc:`one_neuron`

.. GENERATED FROM PYTHON SOURCE LINES 38-41

First, we import all necessary modules for simulation, analysis and plotting.
Additionally, we set the verbosity to suppress info messages and reset
the kernel.

.. GENERATED FROM PYTHON SOURCE LINES 41-49

.. code-block:: default


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

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


.. GENERATED FROM PYTHON SOURCE LINES 50-51

Second, we create the two neurons and the recording device.

.. GENERATED FROM PYTHON SOURCE LINES 51-56

.. code-block:: default


    neuron_1 = nest.Create("iaf_psc_alpha")
    neuron_2 = nest.Create("iaf_psc_alpha")
    voltmeter = nest.Create("voltmeter")


.. GENERATED FROM PYTHON SOURCE LINES 57-58

Third, we set the external current of neuron 1.

.. GENERATED FROM PYTHON SOURCE LINES 58-61

.. code-block:: default


    neuron_1.I_e = 376.0


.. GENERATED FROM PYTHON SOURCE LINES 62-66

Fourth, we connect neuron 1 to neuron 2.
Then, we connect a voltmeter to the two neurons.
To learn more about the previous steps, please check out the
:doc:`one neuron example <one_neuron>`.

.. GENERATED FROM PYTHON SOURCE LINES 66-74

.. code-block:: default


    weight = 20.0
    delay = 1.0

    nest.Connect(neuron_1, neuron_2, syn_spec={"weight": weight, "delay": delay})
    nest.Connect(voltmeter, neuron_1)
    nest.Connect(voltmeter, neuron_2)


.. GENERATED FROM PYTHON SOURCE LINES 75-77

Now we simulate the network using ``Simulate``, which takes the
desired simulation time in milliseconds.

.. GENERATED FROM PYTHON SOURCE LINES 77-80

.. code-block:: default


    nest.Simulate(1000.0)


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Finally, we plot the neurons' membrane potential as a function of
time.

.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. code-block:: default


    nest.voltage_trace.from_device(voltmeter)
    plt.show()


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

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


.. _sphx_glr_download_auto_examples_twoneurons.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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