# Get rid of coordinates already used
pos_list = np.delete(pos_list, inds, 0)
# Pick new coordinates
inds = np.random.choice(np.arange(0, np.size(pos_list, 0)), numBask, replace=False)
pos = pos_list[inds, :]
bask_pos = pos.copy()
nid_pos = np.concatenate([pyra_pos, pyrc_pos, aac_pos, bask_pos])
# import pdb; pdb.set_trace()
# Add a population of numBask nodes
net.add_nodes(N=numBask, pop_name='Bask',
positions=positions_list(positions=pos),
rotation_angle_zaxis=xiter_random(N=numBask, min_x=0.0, max_x=2 * np.pi),
rotation_angle_yaxis=xiter_random(N=numBask, min_x=0.0, max_x=2 * np.pi),
mem_potential='e',
model_type='biophysical',
model_template='hoc:basket',
morphology=None)
################################################################################
############################# BACKGROUND INPUTS ################################
# External inputs
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=numPN_A + numPN_C,
pop_name='tON',
potential='exc',
model_type='virtual')
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 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='point_process',
orig_model='glif',
**model_props)
# Build intfire type cells
for model_props in intfire_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)
