.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/gap_junctions_two_neurons.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_gap_junctions_two_neurons.py: Gap Junctions: Two neuron example --------------------------------- This script simulates two Hodgkin-Huxley neurons of type ``hh_psc_alpha_gap`` connected by a gap junction. Both neurons receive a constant current of 100.0 pA. The neurons are initialized with different membrane potentials and synchronize over time due to the gap-junction connection. .. GENERATED FROM PYTHON SOURCE LINES 32-39 .. code-block:: default import nest import matplotlib.pyplot as plt import numpy nest.ResetKernel() .. GENERATED FROM PYTHON SOURCE LINES 40-42 First we set the resolution of the simulation, create two neurons and create a ``voltmeter`` for recording. .. GENERATED FROM PYTHON SOURCE LINES 42-49 .. code-block:: default nest.resolution = 0.05 neuron = nest.Create('hh_psc_alpha_gap', 2) vm = nest.Create('voltmeter', params={'interval': 0.1}) .. GENERATED FROM PYTHON SOURCE LINES 50-52 Then we set the constant current input, modify the inital membrane potential of one of the neurons and connect the neurons to the ``voltmeter``. .. GENERATED FROM PYTHON SOURCE LINES 52-58 .. code-block:: default neuron.I_e = 100. neuron[0].V_m = -10. nest.Connect(vm, neuron, 'all_to_all') .. GENERATED FROM PYTHON SOURCE LINES 59-63 In order to create the ``gap_junction`` connection we employ the ``all_to_all`` connection rule: Gap junctions are bidirectional connections, therefore we need to connect `neuron[0]` to `neuron[1]` and `neuron[1]` to `neuron[0]`: .. GENERATED FROM PYTHON SOURCE LINES 63-68 .. code-block:: default nest.Connect(neuron, neuron, {'rule': 'all_to_all', 'allow_autapses': False}, {'synapse_model': 'gap_junction', 'weight': 0.5}) .. GENERATED FROM PYTHON SOURCE LINES 69-71 Finally we start the simulation and plot the membrane potentials of both neurons. .. GENERATED FROM PYTHON SOURCE LINES 71-84 .. code-block:: default nest.Simulate(351.) senders = vm.events['senders'] times = vm.events['times'] v_m_values = vm.events['V_m'] plt.figure(1) plt.plot(times[numpy.where(senders == 1)], v_m_values[numpy.where(senders == 1)], 'r-') plt.plot(times[numpy.where(senders == 2)], v_m_values[numpy.where(senders == 2)], 'g-') plt.xlabel('time (ms)') plt.ylabel('membrane potential (mV)') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_auto_examples_gap_junctions_two_neurons.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: gap_junctions_two_neurons.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: gap_junctions_two_neurons.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_