import os import sys from optparse import OptionParser import numpy as np import pandas as pd import h5py from bmtk.builder.networks import NetworkBuilder # Create 10 identical cells where 5 of the receive inputs of synaptic type 'a' and 5 of synaptic type 'b'. # This setup will allow comparing results between two different synaptic types firing_rate = [10,20,50,100,200] def build_net(): net = NetworkBuilder("slice") net.add_nodes(N=5, pop_name='Scnn1a', synapse_model='a', firing_rate=firing_rate, model_type='biophysical', model_template='ctdb:Biophys1.hoc', dynamics_params='472363762_fit.json', morphology='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc', rotation_angle_zaxis=-3.646878266, model_processing='aibs_perisomatic,extracellular') net.add_nodes(N=5, pop_name='Scnn1a', synapse_model='b', firing_rate=firing_rate, model_type='biophysical', model_template='ctdb:Biophys1.hoc', model_processing='aibs_perisomatic,extracellular', dynamics_params='472363762_fit.json', morphology='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc', rotation_angle_zaxis=-3.646878266) net.build() net.save_nodes(nodes_file_name='network/slice_nodes.h5', node_types_file_name='network/slice_node_types.csv') return net # Create 5 external cells each stimulating at different frequency 10,20,50,100,200. # Connect them to the nodes such that each cell connects to one cell of synaptic type 'a' and one cell of type 'b'. def build_ext5_nodes(): if not os.path.exists('network'): os.makedirs('network') EXT = NetworkBuilder("EXT") # need 5 cells to stimulate at 5 different frequencies EXT.add_nodes(N=5, pop_name='EXT', model_type='virtual', firing_rate=firing_rate) # Save cells.csv and cell_types.csv EXT.save_nodes(nodes_file_name='network/ext_nodes.h5', node_types_file_name='network/ext_node_types.csv') net = NetworkBuilder('slice') net.import_nodes(nodes_file_name='network/slice_nodes.h5', node_types_file_name='network/slice_node_types.csv') net.add_edges(source=EXT.nodes(firing_rate=10), target=net.nodes(firing_rate=10,synapse_model='a'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'AMPA_ExcToExc.json', 'model_template': 'expsyn'})) net.add_edges(source=EXT.nodes(firing_rate=20), target=net.nodes(firing_rate=20,synapse_model='a'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'AMPA_ExcToExc.json', 'model_template': 'expsyn'})) net.add_edges(source=EXT.nodes(firing_rate=50), target=net.nodes(firing_rate=50,synapse_model='a'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'AMPA_ExcToExc.json', 'model_template': 'expsyn'})) net.add_edges(source=EXT.nodes(firing_rate=100), target=net.nodes(firing_rate=100,synapse_model='a'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'AMPA_ExcToExc.json', 'model_template': 'expsyn'})) net.add_edges(source=EXT.nodes(firing_rate=200), target=net.nodes(firing_rate=200,synapse_model='a'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'AMPA_ExcToExc.json', 'model_template': 'expsyn'})) net.add_edges(source=EXT.nodes(firing_rate=10), target=net.nodes(firing_rate=10,synapse_model='b'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'pvalb_pvalb.json', 'model_template': 'stp2syn'})) net.add_edges(source=EXT.nodes(firing_rate=20), target=net.nodes(firing_rate=20,synapse_model='b'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'pvalb_pvalb.json', 'model_template': 'stp2syn'})) net.add_edges(source=EXT.nodes(firing_rate=50), target=net.nodes(firing_rate=50,synapse_model='b'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'pvalb_pvalb.json', 'model_template': 'stp2syn'})) net.add_edges(source=EXT.nodes(firing_rate=100), target=net.nodes(firing_rate=100,synapse_model='b'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'pvalb_pvalb.json', 'model_template': 'stp2syn'})) net.add_edges(source=EXT.nodes(firing_rate=200), target=net.nodes(firing_rate=200,synapse_model='b'), connection_rule=5, **({'syn_weight': 0.002, 'weight_function': 'wmax', 'distance_range': [0.0, 50.0], 'target_sections': ['somatic','basal', 'apical'], 'delay': 2.0, 'dynamics_params': 'pvalb_pvalb.json', 'model_template': 'stp2syn'})) net.build() net.save_edges(edges_file_name='network/ext_to_slice_edges.h5', edge_types_file_name='network/ext_to_slice_edge_types.csv') if __name__ == '__main__': parser = OptionParser() parser.add_option("--force-overwrite", dest="force_overwrite", action="store_true", default=False) parser.add_option("--out-dir", dest="out_dir", default='./output/') parser.add_option("--percentage", dest="percentage", type="float", default=1.0) parser.add_option("--with-stats", dest="with_stats", action="store_true", default=False) (options, args) = parser.parse_args() my_network = build_net() build_ext5_nodes()