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update conver_notebooks to produce a json with html outputs organized…
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| # Christopher Bailey <[email protected]> | ||
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| [[plot_simulate_evoked.ipynb]] | ||
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| {"plot_simulate_evoked.ipynb": {"01. Simulate Event Related Potentials (ERPs)": {"contents": "<h1>01. Simulate Event Related Potentials (ERPs)</h1>", "sections": {"References": {"contents": "<h2>References</h2><p>.. [1] Jones, Stephanie R., et al. \"Neural correlates of tactile detection:\n a combined magnetoencephalography and biophysically based computational\n modeling study.\" Journal of Neuroscience 27.40 (2007): 10751-10764.</p>"}}}}} | ||
| { | ||
| "plot_simulate_evoked.ipynb": { | ||
| "01. Simulate Event Related Potentials (ERPs)": { | ||
| "title": "01. Simulate Event Related Potentials (ERPs)", | ||
| "level": 1, | ||
| "html": "<div class='markdown-cell'>\n<h1>01. Simulate Event Related Potentials (ERPs)</h1>\n</div>\n<div class='markdown-cell'>\n<p>This example demonstrates how to simulate a threshold level tactile\nevoked response, as detailed in the <a href=\"https://jonescompneurolab.github.io/hnn-tutorials/erp/erp\">HNN GUI ERP tutorial</a>,\nusing HNN-core. We recommend you first review the GUI tutorial.</p>\n<p>The workflow below recreates an example of the threshold level tactile\nevoked response, as observed in Jones et al. J. Neuroscience 2007 [1]_\n(e.g. Figure 7 in the GUI tutorial), albeit without a direct comparison\nto the recorded data.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\n# Authors: Mainak Jas <[email protected]>\n# Sam Neymotin <[email protected]>\n# Blake Caldwell <[email protected]>\n# Christopher Bailey <[email protected]>\n# sphinx_gallery_thumbnail_number = 3\nimport os.path as op\nimport tempfile\nimport matplotlib.pyplot as plt\n</code>\n</div>\n<div class='markdown-cell'>\n<p>Let us import hnn_core</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nimport hnn_core\nfrom hnn_core import simulate_dipole, jones_2009_model\nfrom hnn_core.viz import plot_dipole\n</code>\n</div>\n<div class='markdown-cell'>\n<p>Let us first create our default network and visualize the cells\ninside it.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nnet = jones_2009_model()\nnet.plot_cells()\nnet.cell_types['L5_pyramidal'].plot_morphology()\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Figure size 640x480 with 1 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_01.png'/>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Figure size 640x480 with 1 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_02.png'/>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Axes3D: >\n</div>\n</div>\n<div class='markdown-cell'>\n<p>The network of cells is now defined, to which we add external drives as\nrequired. Weights are prescribed separately for AMPA and NMDA receptors\n(receptors that are not used can be omitted or set to zero). The possible\ndrive types include the following (click on the links for documentation):</p>\n<ul>\n<li>:meth:<code>hnn_core.Network.add_evoked_drive</code></li>\n<li>:meth:<code>hnn_core.Network.add_poisson_drive</code></li>\n<li>:meth:<code>hnn_core.Network.add_bursty_drive</code></li>\n</ul>\n</div>\n<div class='markdown-cell'>\n<p>First, we add a distal evoked drive</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nweights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n'L5_pyramidal': 0.142300}\nweights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n'L5_pyramidal': 0.080074}\nsynaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n'L5_pyramidal': 0.1}\nnet.add_evoked_drive(\n'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\nweights_nmda=weights_nmda_d1, location='distal',\nsynaptic_delays=synaptic_delays_d1, event_seed=274)\n</code>\n</div>\n<div class='markdown-cell'>\n<p>Then, we add two proximal drives</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nweights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n'L5_basket': 0.19934, 'L5_pyramidal': 0.00865}\nsynaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n'L5_basket': 1., 'L5_pyramidal': 1.}\n# all NMDA weights are zero; pass None explicitly\nnet.add_evoked_drive(\n'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\nweights_nmda=None, location='proximal',\nsynaptic_delays=synaptic_delays_prox, event_seed=544)\n# Second proximal evoked drive. NB: only AMPA weights differ from first\nweights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n'L5_basket': 0.008958, 'L5_pyramidal': 0.684013}\n# all NMDA weights are zero; omit weights_nmda (defaults to None)\nnet.add_evoked_drive(\n'evprox2', mu=137.12, sigma=8.33, numspikes=1,\nweights_ampa=weights_ampa_p2, location='proximal',\nsynaptic_delays=synaptic_delays_prox, event_seed=814)\n</code>\n</div>\n<div class='markdown-cell'>\n<p>Now let's simulate the dipole, running 2 trials with the\n<code>hnn_core.parallel_backends.Joblib</code> backend.\nTo run them in parallel we could set <code>n_jobs</code> to equal the number of\ntrials. The <code>Joblib</code> backend allows running the simulations in parallel\nacross trials.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nfrom hnn_core import JoblibBackend\nwith JoblibBackend(n_jobs=2):\ndpls = simulate_dipole(net, tstop=170., n_trials=2)\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\nJoblib will run 2 trial(s) in parallel by distributing trials over 2 jobs.\nLoading custom mechanism files from /opt/homebrew/Caskroom/miniconda/base/envs/py311/lib/python3.11/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\nBuilding the NEURON model\n[Done]\nTrial 1: 0.03 ms...\nTrial 1: 10.0 ms...\nTrial 1: 20.0 ms...\nTrial 1: 30.0 ms...\nTrial 1: 40.0 ms...\nTrial 1: 50.0 ms...\nTrial 1: 60.0 ms...\nTrial 1: 70.0 ms...\nTrial 1: 80.0 ms...\nTrial 1: 90.0 ms...\nTrial 1: 100.0 ms...\nTrial 1: 110.0 ms...\nTrial 1: 120.0 ms...\nTrial 1: 130.0 ms...\nTrial 1: 140.0 ms...\nTrial 1: 150.0 ms...\nTrial 1: 160.0 ms...\nBuilding the NEURON model\n[Done]\nTrial 2: 0.03 ms...\nTrial 2: 10.0 ms...\nTrial 2: 20.0 ms...\nTrial 2: 30.0 ms...\nTrial 2: 40.0 ms...\nTrial 2: 50.0 ms...\nTrial 2: 60.0 ms...\nTrial 2: 70.0 ms...\nTrial 2: 80.0 ms...\nTrial 2: 90.0 ms...\nTrial 2: 100.0 ms...\nTrial 2: 110.0 ms...\nTrial 2: 120.0 ms...\nTrial 2: 130.0 ms...\nTrial 2: 140.0 ms...\nTrial 2: 150.0 ms...\nTrial 2: 160.0 ms...\n</div>\n</div>\n<div class='markdown-cell'>\n<p>Rather than reading smoothing and scaling parameters from file, we recommend\nexplicit use of the <code>~hnn_core.dipole.Dipole.smooth</code> and\n<code>~hnn_core.dipole.Dipole.scale</code> methods instead. Note that both methods\noperate in-place, i.e., the objects are modified.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nwindow_len, scaling_factor = 30, 3000\nfor dpl in dpls:\ndpl.smooth(window_len).scale(scaling_factor)\n</code>\n</div>\n<div class='markdown-cell'>\n<p>Plot the amplitudes of the simulated aggregate dipole moments over time</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nimport matplotlib.pyplot as plt\nfig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6),\nconstrained_layout=True)\nplot_dipole(dpls, ax=axes[0], layer='agg', show=False)\nnet.cell_response.plot_spikes_hist(ax=axes[1],\nspike_types=['evprox', 'evdist']);\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Figure size 600x600 with 2 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_03.png'/>\n</div>\n<div class='markdown-cell'>\n<p>If you want to analyze how the different cortical layers contribute to\ndifferent net waveform features, then instead of passing <code>&#x27;agg&#x27;</code> to\n<code>layer</code>, you can provide a list of layers to be visualized and optionally\na list of axes to <code>ax</code> to visualize the dipole moments separately.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nplot_dipole(dpls, average=False, layer=['L2', 'L5', 'agg'], show=False);\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Figure size 640x480 with 3 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_04.png'/>\n</div>\n<div class='markdown-cell'>\n<p>Now, let us try to make the exogenous driving inputs to the cells\nsynchronous and see what happens. This is achieved by setting\n<code>n_drive_cells=1</code> and <code>cell_specific=False</code> when adding each drive.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nnet_sync = jones_2009_model()\nn_drive_cells=1\ncell_specific=False\nnet_sync.add_evoked_drive(\n'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\nweights_nmda=weights_nmda_d1, location='distal', n_drive_cells=n_drive_cells,\ncell_specific=cell_specific, synaptic_delays=synaptic_delays_d1, event_seed=274)\nnet_sync.add_evoked_drive(\n'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\nweights_nmda=None, location='proximal', n_drive_cells=n_drive_cells,\ncell_specific=cell_specific, synaptic_delays=synaptic_delays_prox, event_seed=544)\nnet_sync.add_evoked_drive(\n'evprox2', mu=137.12, sigma=8.33, numspikes=1,\nweights_ampa=weights_ampa_p2, location='proximal', n_drive_cells=n_drive_cells,\ncell_specific=cell_specific, synaptic_delays=synaptic_delays_prox, event_seed=814)\n</code>\n</div>\n<div class='markdown-cell'>\n<p>You may interrogate current values defining the spike event time dynamics by</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\nprint(net_sync.external_drives['evdist1']['dynamics'])\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n{'mu': 63.53, 'sigma': 3.85, 'numspikes': 1}\n</div>\n</div>\n<div class='markdown-cell'>\n<p>Finally, let's simulate this network. Rather than modifying the dipole\nobject, this time we make a copy of it before smoothing and scaling.</p>\n</div>\n<div class='code-cell'>\n<code class='language-python'>\ndpls_sync = simulate_dipole(net_sync, tstop=170., n_trials=1)\ntrial_idx = 0\ndpls_sync[trial_idx].copy().smooth(window_len).scale(scaling_factor).plot()\nnet_sync.cell_response.plot_spikes_hist();\n</code>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\nJoblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\nBuilding the NEURON model\n[Done]\nTrial 1: 0.03 ms...\nTrial 1: 10.0 ms...\nTrial 1: 20.0 ms...\nTrial 1: 30.0 ms...\nTrial 1: 40.0 ms...\nTrial 1: 50.0 ms...\nTrial 1: 60.0 ms...\nTrial 1: 70.0 ms...\nTrial 1: 80.0 ms...\nTrial 1: 90.0 ms...\nTrial 1: 100.0 ms...\nTrial 1: 110.0 ms...\nTrial 1: 120.0 ms...\nTrial 1: 130.0 ms...\nTrial 1: 140.0 ms...\nTrial 1: 150.0 ms...\nTrial 1: 160.0 ms...\n<Figure size 640x480 with 1 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_05.png'/>\n</div>\n<div class='output-cell'><div class='output-label'>\nOut:\n</div>\n<div class='output-code'>\n<Figure size 640x480 with 1 Axes>\n</div>\n</div>\n<div class='output-cell'>\n<img src='output_nb_plot_simulate_evoked/fig_06.png'/>\n</div>\n<div class='markdown-cell'>\n<p><h4>Warning</h4></p>\n<ul>\n<li>\n<p>Always look at dipoles in conjunction with raster plots and spike histogram to avoid misinterpretation.</p>\n</li>\n<li>\n<p>Run multiple trials of your simulation to get an average of different drives seeds before drawing conclusions.</p>\n</li>\n</ul>\n</div>", | ||
| "sub-sections": [ | ||
| { | ||
| "title": "References", | ||
| "level": 2, | ||
| "html": "<div class='markdown-cell'>\n<h2>References</h2>\n<p>.. [1] Jones, Stephanie R., et al. "Neural correlates of tactile detection:\na combined magnetoencephalography and biophysically based computational\nmodeling study." Journal of Neuroscience 27.40 (2007): 10751-10764.</p>\n</div>" | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| } |
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