.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/howto/plot_firing_pattern.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_howto_plot_firing_pattern.py: ======================= 01. Plot firing pattern ======================= This example demonstrates how to inspect the firing pattern of cells in the HNN model. .. GENERATED FROM PYTHON SOURCE LINES 9-16 .. code-block:: default # Authors: Mainak Jas # Nick Tolley import os.path as op import tempfile .. GENERATED FROM PYTHON SOURCE LINES 17-18 Let us import ``hnn_core``. .. GENERATED FROM PYTHON SOURCE LINES 18-22 .. code-block:: default import hnn_core from hnn_core import read_spikes, jones_2009_model, simulate_dipole .. GENERATED FROM PYTHON SOURCE LINES 23-25 Now let's build the network. We have used the same weights as in the :ref:`evoked example `. .. GENERATED FROM PYTHON SOURCE LINES 25-29 .. code-block:: default import matplotlib.pyplot as plt net = jones_2009_model() .. GENERATED FROM PYTHON SOURCE LINES 30-35 ``net`` does not have any driving inputs and only defines the local network connectivity. Let us go ahead and first add a distal evoked drive. We need to define the AMPA and NMDA weights for the connections. An "evoked drive" defines inputs that are normally distributed with a certain mean and standard deviation. .. GENERATED FROM PYTHON SOURCE LINES 35-47 .. code-block:: default weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': 7e-6, 'L5_pyramidal': 0.142300} weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317, 'L5_pyramidal': 0.080074} synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1, 'L5_pyramidal': 0.1} net.add_evoked_drive( 'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1, weights_nmda=weights_nmda_d1, location='distal', synaptic_delays=synaptic_delays_d1, event_seed=274) .. GENERATED FROM PYTHON SOURCE LINES 48-52 The reason it is called an "evoked drive" is it can be used to simulate waveforms resembling evoked responses. Here, we show how to do it with two proximal drives which drive current up the dendrite and one distal drive which drives current down the dendrite producing the negative deflection. .. GENERATED FROM PYTHON SOURCE LINES 52-63 .. code-block:: default weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525, 'L5_basket': 0.19934, 'L5_pyramidal': 0.00865} synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1, 'L5_basket': 1., 'L5_pyramidal': 1.} # all NMDA weights are zero; pass None explicitly net.add_evoked_drive( 'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1, weights_nmda=None, location='proximal', synaptic_delays=synaptic_delays_prox, event_seed=544) .. GENERATED FROM PYTHON SOURCE LINES 64-67 Now we add the second proximal evoked drive and simulate the network dynamics with somatic voltage recordings enabled. Note: only AMPA weights differ from first. .. GENERATED FROM PYTHON SOURCE LINES 67-77 .. code-block:: default weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840, 'L5_basket': 0.008958, 'L5_pyramidal': 0.684013} # all NMDA weights are zero; omit weights_nmda (defaults to None) net.add_evoked_drive( 'evprox2', mu=137.12, sigma=8.33, numspikes=1, weights_ampa=weights_ampa_p2, location='proximal', synaptic_delays=synaptic_delays_prox, event_seed=814) dpls = simulate_dipole(net, tstop=170., record_vsec='soma') .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... .. GENERATED FROM PYTHON SOURCE LINES 78-82 Here, we explain more details about the data structures and how they can be used to better interpret the data. The cell IDs (gids) uniquely define neurons in the network and are stored in the :class:`~hnn_core.Network` object as a dictionary .. GENERATED FROM PYTHON SOURCE LINES 82-85 .. code-block:: default gid_ranges = net.gid_ranges print(net.gid_ranges) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none {'L2_basket': range(0, 35), 'L2_pyramidal': range(35, 135), 'L5_basket': range(135, 170), 'L5_pyramidal': range(170, 270), 'evdist1': range(270, 505), 'evprox1': range(505, 775), 'evprox2': range(775, 1045)} .. GENERATED FROM PYTHON SOURCE LINES 86-90 Simulated voltage in the soma and other cell sections are stored in :class:`~hnn_core.CellResponse` as a dictionary. The CellResponse object stores data produced by individual cells including spikes, voltages and currents. .. GENERATED FROM PYTHON SOURCE LINES 90-94 .. code-block:: default trial_idx = 0 vsec = net.cell_response.vsec[trial_idx] print(vsec.keys()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269]) .. GENERATED FROM PYTHON SOURCE LINES 95-96 We can plot the firing pattern of individual cells by indexing with the gid .. GENERATED FROM PYTHON SOURCE LINES 96-104 .. code-block:: default gid = 170 plt.figure(figsize=(4, 4), constrained_layout=True) plt.plot(net.cell_response.times, vsec[gid]['soma']) plt.title('%s (gid=%d)' % (net.gid_to_type(gid), gid)) plt.xlabel('Time (ms)') plt.ylabel('Voltage (mV)') plt.show() .. image-sg:: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_001.png :alt: L5_pyramidal (gid=170) :srcset: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 105-109 Also, we can plot the spikes in the network and write them to text files. Note that we can use formatting syntax to specify the filename pattern with which each trial will be written ('spk_1.txt', 'spk_2.txt, ...). To read spikes back in, we can use wildcard expressions. .. GENERATED FROM PYTHON SOURCE LINES 109-115 .. code-block:: default net.cell_response.plot_spikes_raster() with tempfile.TemporaryDirectory() as tmp_dir_name: net.cell_response.write(op.join(tmp_dir_name, 'spk_%d.txt')) cell_response = read_spikes(op.join(tmp_dir_name, 'spk_*.txt')) cell_response.plot_spikes_raster() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_002.png :alt: plot firing pattern :srcset: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_003.png :alt: plot firing pattern :srcset: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_003.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Writing file /tmp/tmplwf9zwuk/spk_0.txt
.. GENERATED FROM PYTHON SOURCE LINES 116-118 We can additionally calculate the mean spike rates for each cell class by specifying a time window with ``tstart`` and ``tstop``. .. GENERATED FROM PYTHON SOURCE LINES 118-129 .. code-block:: default all_rates = cell_response.mean_rates(tstart=0, tstop=170, gid_ranges=net.gid_ranges, mean_type='all') trial_rates = cell_response.mean_rates(tstart=0, tstop=170, gid_ranges=net.gid_ranges, mean_type='trial') print('Mean spike rates across trials:') print(all_rates) print('Mean spike rates for individual trials:') print(trial_rates) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Mean spike rates across trials: {'L2_basket': 12.26890756302521, 'L2_pyramidal': 4.411764705882352, 'L5_basket': 15.966386554621847, 'L5_pyramidal': 22.64705882352941} Mean spike rates for individual trials: {'L2_basket': [12.26890756302521], 'L2_pyramidal': [4.411764705882352], 'L5_basket': [15.966386554621847], 'L5_pyramidal': [22.64705882352941]} .. GENERATED FROM PYTHON SOURCE LINES 130-132 Finally, we can plot the soma voltage along with the spiking activity with raster plots and histograms for the pyramidal cells. .. GENERATED FROM PYTHON SOURCE LINES 132-144 .. code-block:: default fig, axes = plt.subplots(3, 1, figsize=(5, 7), sharex=True) for idx in range(10): # only 10 cells per cell-type gid = gid_ranges['L2_pyramidal'][idx] axes[0].plot(net.cell_response.times, vsec[gid]['soma'], color='g') gid = gid_ranges['L5_pyramidal'][idx] axes[0].plot(net.cell_response.times, vsec[gid]['soma'], color='r') net.cell_response.plot_spikes_raster(ax=axes[1]) net.cell_response.plot_spikes_hist(ax=axes[2], spike_types=['L5_pyramidal', 'L2_pyramidal']) .. image-sg:: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_004.png :alt: plot firing pattern :srcset: /auto_examples/howto/images/sphx_glr_plot_firing_pattern_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none
.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 59.264 seconds) .. _sphx_glr_download_auto_examples_howto_plot_firing_pattern.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/jonescompneurolab/hnn-core/gh-pages?filepath=v0.3/notebooks/auto_examples/howto/plot_firing_pattern.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_firing_pattern.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_firing_pattern.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_