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.. "auto_examples/repeated_stimulation.py"
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.. only:: html

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

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

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

.. _sphx_glr_auto_examples_repeated_stimulation.py:


Repeated Stimulation
--------------------

Simple example for how to repeat a stimulation protocol
using the ``origin`` property of devices.

In this example, a ``poisson_generator`` generates a spike train that is
recorded directly by a ``spike_recorder``, using the following paradigm:

1. A single trial last for 1000 ms.
2. Within each trial, the ``poisson_generator`` is active from 100 ms to 500 ms.

We achieve this by defining the `start` and `stop` properties of the
generator to 100 ms and 500 ms, respectively, and setting the ``origin`` to the
simulation time at the beginning of each trial. Start and stop are interpreted
relative to the ``origin``.

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First, the modules needed for simulation and analysis are imported.

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.. code-block:: default



    import nest
    import nest.raster_plot
    import matplotlib.pyplot as plt


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Second, we set the parameters so the ``poisson_generator`` generates 1000
spikes per second and is active from 100 to 500 ms

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.. code-block:: default



    rate = 1000.0  # generator rate in spikes/s
    start = 100.0  # start of simulation relative to trial start, in ms
    stop = 500.0   # end of simulation relative to trial start, in ms



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The simulation is supposed to take 1s (1000 ms) and is repeated 5 times

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.. code-block:: default



    trial_duration = 1000.0  # trial duration, in ms
    num_trials = 5           # number of trials to perform



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Third, the network is set up.  We reset the kernel and create a
``poisson_generator``, in which the handle is stored in `pg`.

The parameters for rate and start and stop of activity are given as optional
parameters in the form of a dictionary.

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.. code-block:: default



    nest.ResetKernel()
    pg_params = {'rate': rate, 'start': start, 'stop': stop}
    pg = nest.Create('poisson_generator', params=pg_params)



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The ``spike_recorder`` is created and the handle stored in `sr`.

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.. code-block:: default



    sr = nest.Create('spike_recorder')



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The ``Connect`` function connects the nodes so spikes from pg are collected by
the ``spike_recorder`` `sr`

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.. code-block:: default



    nest.Connect(pg, sr)



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Before each trial, we set the ``origin`` of the ``poisson_generator`` to the
current simulation time. This automatically sets the start and stop time of
the ``poisson_generator`` to the specified times with respect to the origin.
The simulation is then carried out for the specified time in trial_duration.

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.. code-block:: default



    for n in range(num_trials):
        pg.origin = nest.biological_time
        nest.Simulate(trial_duration)



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Now we plot the result, including a histogram using the ``nest.raster_plot``
function. Note: The histogram will show spikes seemingly located before
100 ms into each trial. This is due to sub-optimal automatic placement of
histogram bin borders.

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.. code-block:: default


    nest.raster_plot.from_device(sr, hist=True, hist_binwidth=100.,
                                 title='Repeated stimulation by Poisson generator')
    plt.show()


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

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


.. _sphx_glr_download_auto_examples_repeated_stimulation.py:

.. only:: html

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


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

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

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

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


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