import numpy as np
import math
import random
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.aux.node_params import positions_columinar, xiter_random
from bmtk.builder.aux.edge_connectors import distance_connector
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='L4',
ei='e',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a.swc',
set_params_function='Biophys1')
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='L4',
ei='i',
level_of_detail='biophysical',
params_file='472912177_fit.json',
morphology_file='Pvalb.swc',
set_params_function='Biophys1')
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.aux.node_params import positions_columinar, xiter_random
from bmtk.builder.aux.edge_connectors import distance_connector
import math
import numpy as np
import random
cortex = NetworkBuilder('mcortex')
cortex.add_nodes(N=100,
pop_name='Scnn1a',
positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=3.646878266,
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472363762_fit.json',
morphology='Scnn1a_473845048_m.swc')
cortex.add_edges(source={'pop_name': 'Scnn1a'}, target={'pop_name': 'Scnn1a'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 50.0, 'nsyn_min': 0, 'nsyn_max': 10},
syn_weight=2.0e-04,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical', 'soma'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
n_cells = 80 if model_props[
'ei'] == 'e' else 30 # 80% excitatory, 20% inhib
# Randomly get positions uniformly distributed in a column
positions = positions_columinar(N=n_cells,
center=[0, 10.0, 0],
max_radius=50.0,
height=200.0)
internal.add_nodes(
N=n_cells,
x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
rotation_angle_yaxis=xiter_random(N=n_cells,
min_x=0.0,
max_x=2 *
np.pi), # randomly rotate y axis
rotation_angle_zaxis=xiter_random(N=n_cells,
min_x=0.0,
max_x=2 * np.pi), #
model_type='biophysical',
model_processing='aibs_perisomatic',
**model_props)
# Build intfire type cells
for model_props in point_models:
n_cells = 75 # Just assume 75 cells for both point inhibitory and point excitatory
positions = positions_columinar(N=n_cells,
center=[0, 10.0, 0],
max_radius=50.0,
height=200.0)