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import numpy as np
import math
import random
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.auxi.node_params import positions_columinar, xiter_random
from bmtk.builder.auxi.edge_connectors import distance_connector
import os
net = NetworkBuilder("V1")
net.add_nodes(N=80, pop_name='Scnn1a',
positions=positions_columinar(N=80, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),
tuning_angle=np.linspace(start=0.0, stop=360.0, num=80, endpoint=False),
location='VisL4',
ei='e',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472363762_fit.json',
morphology='Scnn1a.swc')
net.add_nodes(N=20, pop_name='PV',
positions=positions_columinar(N=20, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),
location='VisL4',
ei='i',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472912177_fit.json',
morphology='Pvalb.swc')
net.add_nodes(N=200, pop_name='LIF_exc',
positions=positions_columinar(N=200, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),
tuning_angle=np.linspace(start=0.0, stop=360.0, num=200, endpoint=False),
location='VisL4',
ei='e',
model_type='point_process',
model_template='nrn:IntFire1',
dynamics_params='IntFire1_exc_1.json')
net.add_nodes(N=100, pop_name='LIF_inh',
positions=positions_columinar(N=100, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),
location='VisL4',
ei='i',
model_type='point_process',
model_template='nrn:IntFire1',
dynamics_params='IntFire1_inh_1.json')
## Generating E-to-E connections
def dist_tuning_connector(source, target, d_weight_min, d_weight_max, d_max, t_weight_min, t_weight_max, nsyn_min,
nsyn_max):
if source['node_id'] == target['node_id']:
# Avoid self-connections.n_nodes
return None
r = np.linalg.norm(np.array(source['positions']) - np.array(target['positions']))
if r > d_max:
dw = 0.0
else:
t = r / d_max
dw = d_weight_max * (1.0 - t) + d_weight_min * t
if dw <= 0:
# drop the connection if the weight is too low
return None
# next create weights by orientation tuning [ aligned, misaligned ] --> [ 1, 0 ], Check that the orientation
# tuning property exists for both cells; otherwise, ignore the orientation tuning.
if 'tuning_angel' in source and 'tuning_angle' in target:
# 0-180 is the same as 180-360, so just modulo by 180
delta_tuning = math.fmod(abs(source['tuning_angle'] - target['tuning_angle']), 180.0)
# 90-180 needs to be flipped, then normalize to 0-1
delta_tuning = delta_tuning if delta_tuning < 90.0 else 180.0 - delta_tuning
t = delta_tuning / 90.0
tw = t_weight_max * (1.0 - t) + t_weight_min * t
else:
tw = dw
# drop the connection if the weight is too low
if tw <= 0:
return None
# filter out nodes by treating the weight as a probability of connection
if random.random() > tw:
return None
# Add the number of synapses for every connection.
# It is probably very useful to take this out into a separate function.
return random.randint(nsyn_min, nsyn_max)
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Scnn1a'},
connection_rule=dist_tuning_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 't_weight_min': 0.5,
't_weight_max': 1.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=6.4e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},
connection_rule=dist_tuning_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 't_weight_min': 0.5,
't_weight_max': 1.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.0019,
weight_function='gaussianLL',
weight_sigma=50.0,
delay=2.0,
dynamics_params='instantaneousExc.json',
model_template='exp2syn')
### Generating I-to-I connections
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'biophysical'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.0002,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='GABA_InhToInh.json',
model_template='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'point_process'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.00225,
weight_function='wmax',
delay=2.0,
dynamics_params='instantaneousInh.json',
model_template='exp2syn')
### Generating I-to-E connections
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'biophysical'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.00018,
weight_function='wmax',
distance_range=[0.0, 50.0],
target_sections=['somatic', 'basal', 'apical'],
delay=2.0,
dynamics_params='GABA_InhToExc.json',
model_template='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'point_process'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.009,
weight_function='wmax',
delay=2.0,
dynamics_params='instantaneousInh.json',
model_template='exp2syn')
### Generating E-to-I connections
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.00035,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=0.0043,
weight_function='wmax',
delay=2.0,
dynamics_params='instantaneousExc.json',
model_template='exp2syn')
net.build()
net.save_nodes(output_dir='network')
net.save_edges(output_dir='network')
### Build Thalamus
lgn = NetworkBuilder('LGN')
lgn.add_nodes(
N=500,
pop_name='tON',
potential='exc',
model_type='virtual'
)
v1 = NetworkBuilder('V1')
v1.import_nodes(nodes_file_name='network/V1_nodes.h5', node_types_file_name='network/V1_node_types.csv')
def select_source_cells(sources, target, nsources_min=10, nsources_max=30, nsyns_min=3, nsyns_max=12):
total_sources = len(sources)
nsources = np.random.randint(nsources_min, nsources_max)
selected_sources = np.random.choice(total_sources, nsources, replace=False)
syns = np.zeros(total_sources)
syns[selected_sources] = np.random.randint(nsyns_min, nsyns_max, size=nsources)
return syns
lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='Scnn1a'),
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'nsources_min': 10, 'nsources_max': 25},
syn_weight=4e-03,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='PV1'),
connection_rule=select_source_cells,
connection_params={'nsources_min': 15, 'nsources_max': 30},
iterator='all_to_one',
syn_weight=0.001,
weight_function='wmax',
distance_range=[0.0, 1.0e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_exc'),
connection_rule=select_source_cells,
connection_params={'nsources_min': 10, 'nsources_max': 25},
iterator='all_to_one',
syn_weight= 0.045,
weight_function='wmax',
delay=2.0,
dynamics_params='instantaneousExc.json',
model_template='exp2syn')
lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_inh'),
connection_rule=select_source_cells,
connection_params={'nsources_min': 15, 'nsources_max': 30},
iterator='all_to_one',
syn_weight=0.02,
weight_function='wmax',
delay=2.0,
dynamics_params='instantaneousExc.json',
model_template='exp2syn')
lgn.build()
lgn.save_nodes(output_dir='network')
lgn.save_edges(output_dir='network')
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