.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/sinusoidal_poisson_generator.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_sinusoidal_poisson_generator.py: Sinusoidal poisson generator example ------------------------------------ This script demonstrates the use of the ``sinusoidal_poisson_generator`` and its different parameters and modes. The source code of the model can be found in ``models/sinusoidal_poisson_generator.h``. The script is structured into two parts and creates one common figure. In Part 1, two instances of the ``sinusoidal_poisson_generator`` are created with different parameters. Part 2 illustrates the effect of the ``individual_spike_trains`` switch. .. GENERATED FROM PYTHON SOURCE LINES 40-41 We import the modules required to simulate, analyze and plot this example. .. GENERATED FROM PYTHON SOURCE LINES 41-50 .. code-block:: default import nest import matplotlib.pyplot as plt import numpy as np nest.ResetKernel() # in case we run the script multiple times from iPython .. GENERATED FROM PYTHON SOURCE LINES 51-55 We create two instances of the ``sinusoidal_poisson_generator`` with two different parameter sets using ``Create``. Moreover, we create devices to record firing rates (``multimeter``) and spikes (``spike_recorder``) and connect them to the generators using ``Connect``. .. GENERATED FROM PYTHON SOURCE LINES 55-75 .. code-block:: default nest.resolution = 0.01 num_nodes = 2 g = nest.Create('sinusoidal_poisson_generator', n=num_nodes, params={'rate': [10000.0, 0.0], 'amplitude': [5000.0, 10000.0], 'frequency': [10.0, 5.0], 'phase': [0.0, 90.0]}) m = nest.Create('multimeter', num_nodes, {'interval': 0.1, 'record_from': ['rate']}) s = nest.Create('spike_recorder', num_nodes) nest.Connect(m, g, 'one_to_one') nest.Connect(g, s, 'one_to_one') print(m.get()) nest.Simulate(200) .. GENERATED FROM PYTHON SOURCE LINES 76-78 After simulating, the spikes are extracted from the ``spike_recorder`` and plots are created with panels for the PST and ISI histograms. .. GENERATED FROM PYTHON SOURCE LINES 78-102 .. code-block:: default colors = ['b', 'g'] for j in range(num_nodes): ev = m[j].events t = ev['times'] r = ev['rate'] spike_times = s[j].events['times'] plt.subplot(221) h, e = np.histogram(spike_times, bins=np.arange(0., 201., 5.)) plt.plot(t, r, color=colors[j]) plt.step(e[:-1], h * 1000 / 5., color=colors[j], where='post') plt.title('PST histogram and firing rates') plt.ylabel('Spikes per second') plt.subplot(223) plt.hist(np.diff(spike_times), bins=np.arange(0., 1.005, 0.02), histtype='step', color=colors[j]) plt.title('ISI histogram') .. GENERATED FROM PYTHON SOURCE LINES 103-104 The kernel is reset and the number of threads set to 4. .. GENERATED FROM PYTHON SOURCE LINES 104-110 .. code-block:: default nest.ResetKernel() nest.local_num_threads = 4 .. GENERATED FROM PYTHON SOURCE LINES 111-115 A ``sinusoidal_poisson_generator`` with ``individual_spike_trains`` set to `True` is created and connected to 20 parrot neurons whose spikes are recorded by a ``spike_recorder``. After simulating, a raster plot of the spikes is created. .. GENERATED FROM PYTHON SOURCE LINES 115-136 .. code-block:: default g = nest.Create('sinusoidal_poisson_generator', params={'rate': 100.0, 'amplitude': 50.0, 'frequency': 10.0, 'phase': 0.0, 'individual_spike_trains': True}) p = nest.Create('parrot_neuron', 20) s = nest.Create('spike_recorder') nest.Connect(g, p, 'all_to_all') nest.Connect(p, s, 'all_to_all') nest.Simulate(200) ev = s.events plt.subplot(222) plt.plot(ev['times'], ev['senders'] - min(ev['senders']), 'o') plt.ylim([-0.5, 19.5]) plt.yticks([]) plt.title('Individual spike trains for each target') .. GENERATED FROM PYTHON SOURCE LINES 137-141 The kernel is reset again and the whole procedure is repeated for a ``sinusoidal_poisson_generator`` with `individual_spike_trains` set to `False`. The plot shows that in this case, all neurons receive the same spike train from the ``sinusoidal_poisson_generator``. .. GENERATED FROM PYTHON SOURCE LINES 141-164 .. code-block:: default nest.ResetKernel() nest.local_num_threads = 4 g = nest.Create('sinusoidal_poisson_generator', params={'rate': 100.0, 'amplitude': 50.0, 'frequency': 10.0, 'phase': 0.0, 'individual_spike_trains': False}) p = nest.Create('parrot_neuron', 20) s = nest.Create('spike_recorder') nest.Connect(g, p, 'all_to_all') nest.Connect(p, s, 'all_to_all') nest.Simulate(200) ev = s.events plt.subplot(224) plt.plot(ev['times'], ev['senders'] - min(ev['senders']), 'o') plt.ylim([-0.5, 19.5]) plt.yticks([]) plt.title('One spike train for all targets') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_auto_examples_sinusoidal_poisson_generator.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: sinusoidal_poisson_generator.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: sinusoidal_poisson_generator.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_