![\"111bf96ba1904babbd871ade066c28af\"](\"_images/Filters_sp_t_mod.png\")
![\"Responses\"](\"_images/Responses_to_diff_scales_and_orientations.png\")
\n Now we will build a larger bank of filters with randomly assigned properties. Beware that if n_filts is set larger, the code could take a long time to run in the notebook.
\n[3]:\n n_filts = 100\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -138,15 +138,15 @@\n allow for faster onset of responses and a slower tail decay.\n Filters with very little spectral modulation have a \u201cvertical\u201d appearance and\n respond preferentially to broadband temporal edges such as sound onsets.\n Filters with very little temporal modulation have a \u201chorizontal\u201d appearance and\n respond preferentially to sustained spectral edges. If the nodes are ordered by\n their center frequencies, we can construct different \u201cviews\u201d of the stimulus\n (speech in this case) through these different types of filters.\n-[111bf96ba1904babbd871ade066c28af]\n+[338c195dfb2e42999f286421430332ad]\n [Responses]\n Now we will build a larger bank of filters with randomly assigned properties.\n Beware that if n_filts is set larger, the code could take a long time to run in\n the notebook.\n [3]:\n n_filts = 100\n \n"}]}, {"source1": "./usr/share/doc/python3-bmtk-doc/html/tutorial_bionet_disconnected_sims.html", "source2": "./usr/share/doc/python3-bmtk-doc/html/tutorial_bionet_disconnected_sims.html", "unified_diff": "@@ -619,15 +619,15 @@\n \n \n \n Replaying Parts of a Simulation\u00b6
\n When simulating a bio-realistic network, cells will recieve synaptic stimulation from both locally recurrent connections as-well-as feedforward connections from external inputs. Often when analyzing the results of a full network activity we would like to know the contribution of only a subset of the synaptic activity. For example, how much does the feedforward synapses, or only recurrent synapses between specific population of cells, contributed to the simulation results. Certain techniques,\n like running with only a subset of the full network, or using optogenetic/current-clamping to turn on-off subpoluations, can provide useful insights but also not tell the full story of a network simulation.
\n Instead we can used the BMTK \u201creplay\u201d input module to disentangle subsections of a simulation activity from the full network in BioNet/biophysically realistic simulations. The BMTK \u201creplay\u201d module let\u2019s the user take a previous simulation, and replay a simulation but using only activity for only a subset of the synapses. This can be helpful in parameter tuning and optimization, and for very large networks can provide an efficient manner to replay small subsets of a full network.
\n-![\"5ca3ad877c1544d3b20dccf8ad211583\"](\"_images/disconnected_sim_figure.png\")
\n+
\n \n [1]:\n
\n \n from bmtk.simulator import bionet\n from bmtk.analyzer.spike_trains import plot_raster\n
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -14,15 +14,15 @@\n Instead we can used the BMTK \u201creplay\u201d input module to disentangle subsections\n of a simulation activity from the full network in BioNet/biophysically\n realistic simulations. The BMTK \u201creplay\u201d module let\u2019s the user take a previous\n simulation, and replay a simulation but using only activity for only a subset\n of the synapses. This can be helpful in parameter tuning and optimization, and\n for very large networks can provide an efficient manner to replay small subsets\n of a full network.\n-[5ca3ad877c1544d3b20dccf8ad211583]\n+[6e2a4d6b3a1f439daabdf59246741486]\n [1]:\n from bmtk.simulator import bionet\n from bmtk.analyzer.spike_trains import plot_raster\n *\b**\b**\b**\b**\b* I\bIn\bni\bit\bti\bia\bal\bl S\bSi\bim\bmu\bul\bla\bat\bti\bio\bon\bn (\b(G\bGe\ben\bne\ber\bra\bat\bti\bin\bng\bg a\ba B\bBa\bas\bse\bel\bli\bin\bne\be f\bfo\bor\br S\bSy\byn\bna\bap\bpt\bti\bic\bc A\bAc\bct\bti\biv\bvi\bit\bty\by)\b)_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n First step is to take an existing network + simulation or build one from\n scratch. For more information on how to build and run BioNet simulations please\n see existing _\bt_\bu_\bt_\bo_\br_\bi_\ba_\bl_\bs. For our example we copy the _\bb_\bi_\bo_\bn_\be_\bt_\b__\b4_\b5_\b0_\bc_\be_\bl_\bl_\b _\be_\bx_\ba_\bm_\bp_\bl_\be\n"}]}]}]}]}]}