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build_network.py
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from bmtk.builder import NetworkBuilder
import numpy as np
import sys
import synapses
synapses.load()
syn = synapses.syn_params_dicts()
# Initialize our network
net = NetworkBuilder("biophysical")
#num_inh = [int(lognormal(43, 13)) for i in range(N)]
num_inh = [1]
#num_exc = [int(lognormal(25, 10)) for i in range(N)]
num_exc = [1]
##################################################################################
###################################Pyr Type C#####################################
net.add_nodes(N=5, pop_name='PyrA',
mem_potential='e',
model_type='biophysical',
model_template='hoc:feng_typeA',
morphology=None)
net.add_nodes(N=3, pop_name='PyrC',
mem_potential='e',
model_type='biophysical',
model_template='hoc:feng_typeC',
morphology=None)
net.add_nodes(N=2, pop_name='Int',
mem_potential='e',
model_type='biophysical',
model_template='hoc:basket',
morphology=None)
##################################################################################
###################################External Networks##############################
#print("Internal nodes built")
print("making {} exc_stim nodes".format(np.sum(num_exc)))
# External excitatory inputs
tone = NetworkBuilder('tone')
tone.add_nodes(N=np.sum(num_exc),
pop_name='tone',
potential='exc',
model_type='virtual')
print("making {} inh_stim nodes".format(np.sum(num_inh)))
# External inhibitory inputs
shock = NetworkBuilder('shock')
shock.add_nodes(N=np.sum(num_inh),
pop_name='shock',
potential='exc',
model_type='virtual')
##################################################################################
###################################Edges##########################################
def one_to_one(source, target):
sid = source.node_id
tid = target.node_id
if sid == tid:
#print("connecting cell {} to {}".format(sid,tid))
tmp_nsyn = 1
else:
return None
return tmp_nsyn
# Create connections between Shock --> Pyr cells
net.add_edges(source=shock.nodes(), target=net.nodes(pop_name='PyrA'),
connection_rule=3,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='shock2PN.json',
model_template=syn['shock2PN.json']['level_of_detail'])
net.add_edges(source=shock.nodes(), target=net.nodes(pop_name='PyrC'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='shock2PN.json',
model_template=syn['shock2PN.json']['level_of_detail'])
net.add_edges(source=shock.nodes(), target=net.nodes(pop_name='Int'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='shock2PN.json',
model_template=syn['shock2PN.json']['level_of_detail'])
# Create connections between Tone --> Pyr cells
net.add_edges(source=tone.nodes(), target=net.nodes(pop_name='PyrA'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0, 11.0],
dynamics_params='tone2PN.json',
model_template=syn['tone2PN.json']['level_of_detail'])
net.add_edges(source=tone.nodes(), target=net.nodes(pop_name='PyrC'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0, 11.0],
dynamics_params='tone2PN.json',
model_template=syn['tone2PN.json']['level_of_detail'])
net.add_edges(source=tone.nodes(), target=net.nodes(pop_name='Int'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0, 11.0],
dynamics_params='tone2PN.json',
model_template=syn['tone2PN.json']['level_of_detail'])
# Create connections between Pyr --> Pyr cells
net.add_edges(source=net.nodes(pop_name='PyrA'), target=net.nodes(pop_name='PyrA'),
connection_rule=20,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToExc.json',
model_template=syn['AMPA_ExcToExc.json']['level_of_detail'])
net.add_edges(source=net.nodes(pop_name='PyrA'), target=net.nodes(pop_name='PyrC'),
connection_rule=15,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToExc.json',
model_template=syn['AMPA_ExcToExc.json']['level_of_detail'])
net.add_edges(source=net.nodes(pop_name='PyrC'), target=net.nodes(pop_name='PyrA'),
connection_rule=15,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToExc.json',
model_template=syn['AMPA_ExcToExc.json']['level_of_detail'])
net.add_edges(source=net.nodes(pop_name='PyrC'), target=net.nodes(pop_name='PyrC'),
connection_rule=6,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToExc.json',
model_template=syn['AMPA_ExcToExc.json']['level_of_detail'])
#Create connections between Pyr --> Int cells
net.add_edges(source=net.nodes(pop_name='PyrA'), target=net.nodes(pop_name='Int'),
connection_rule=10,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToInh.json',
model_template=syn['AMPA_ExcToInh.json']['level_of_detail'])
net.add_edges(source=net.nodes(pop_name='PyrC'), target=net.nodes(pop_name='Int'),
connection_rule=6,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='AMPA_ExcToInh.json',
model_template=syn['AMPA_ExcToInh.json']['level_of_detail'])
#Create connections Int --> Int cells
net.add_edges(source=net.nodes(pop_name='Int'), target=net.nodes(pop_name='Int'),
connection_rule=2,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='GABA_InhToInh.json',
model_template=syn['GABA_InhToInh.json']['level_of_detail'])
#Create connections Int --> Pyr cells
net.add_edges(source=net.nodes(pop_name='Inta'), target=net.nodes(pop_name='PyrA'),
connection_rule=10,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='GABA_InhToExc.json',
model_template=syn['GABA_InhToExc.json']['level_of_detail'])
net.add_edges(source=net.nodes(pop_name='Int'), target=net.nodes(pop_name='PyrC'),
connection_rule=6,
syn_weight=1.0,
target_sections=['somatic'],
delay=0.1,
distance_range=[10.0,11.0],
dynamics_params='GABA_InhToExc.json',
model_template=syn['GABA_InhToExc.json']['level_of_detail'])
# Build and save our networks
net.build()
net.save_nodes(output_dir='network')
net.save_edges(output_dir='network')
tone.build()
tone.save_nodes(output_dir='network')
shock.build()
shock.save_nodes(output_dir='network')
#from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator
#from bmtk.utils.reports.spike_trains.spikes_file_writers import write_csv
#exc_psg = PoissonSpikeGenerator(population='exc_stim')
#exc_psg.add(node_ids=range(np.sum(num_exc)),
# firing_rate=int(exc_fr) / 1000,
# times=(200.0, 500.0))
#exc_psg.to_sonata('exc_stim_spikes.h5')
#inh_psg = PoissonSpikeGenerator(population='inh_stim')
#inh_psg.add(node_ids=range(np.sum(num_inh)),
# firing_rate=int(inh_fr) / 1000,
# times=(200.0, 1200.0))
#inh_psg.to_sonata('inh_stim_spikes.h5')
from bmtk.utils.sim_setup import build_env_bionet
build_env_bionet(base_dir='./',
network_dir='./network',
tstop=4000.0, dt = 0.1,
report_vars=['v','cai'],
spikes_inputs=[('tone', 'tone_spikes.csv'), ('shock', 'shock_spikes.csv')],
components_dir='biophys_components',
compile_mechanisms=False)