def test_positions_columinar():
np.random.seed(1)
points = positions_columinar(N=10, center=[0.0, 0.0, 0.0], height=10.0, min_radius=1.0, max_radius=2.0)
assert(points.shape == (10, 3))
# check height restriction
ys = points[:, 1]
assert(np.min(ys) >= -5.0)
assert(np.max(ys) <= 5.0)
# check radius restrictions
xz_plane = points[:,[0,2]]
dists = np.linalg.norm(xz_plane, axis=1)
assert(np.min(dists) >= 1.0)
assert(np.max(dists) <= 2.0)
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')
}, {
'model_name': 'LIF_inh',
'ei': 'i',
'model_template': 'nest:iaf_psc_alpha',
'dynamics_params': 'IntFire1_inh_point.json'
}]
# Build a network of 300 glif cells to simulate
internal = NetworkBuilder("internal")
for i, model_props in enumerate(glif_models):
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',
},
'LIF_inh': {
'N': 40,
'ei': 'i',
'pop_name': 'LIF_inh',
'model_type': 'point_process',
'model_template': 'nest:iaf_psc_delta',
'dynamics_params': 'iaf_psc_delta_inh.json'
}
}
net = NetworkBuilder('cortex')
for model in LIF_models:
params = LIF_models[model].copy()
positions = positions_columinar(N=LIF_models[model]['N'], center=[0, 10.0, 0], max_radius=50.0, height=200.0)
net.add_nodes(x=positions[:, 0], y=positions[:, 1], z=positions[:, 2],
**params)
net.add_edges(source={'ei': 'e'},
connection_rule=random_connections,
connection_params={'p': 0.1},
syn_weight=2.0,
delay=1.5,
dynamics_params='ExcToInh.json',
model_template='static_synapse')
net.add_edges(source={'ei': 'i'},
connection_rule=random_connections,
connection_params={'p': 0.1},
def build_lif_network():
### Define all the cell models in a dictionary.
print('Building Internal Network')
LIF_models = {
'LIF_exc': {
'N': N_LIF_exc,
'ei': 'e',
'pop_name': 'LIF_exc',
'model_type': 'point_process',
'model_template': 'nest:iaf_psc_exp',
'dynamics_params': 'excitatory.json'
},
'LIF_inh': {
'N': N_LIF_inh,
'ei': 'i',
'pop_name': 'LIF_inh',
'model_type': 'point_process',
'model_template': 'nest:iaf_psc_exp',
'dynamics_params': 'inhibitory.json'
}
}
net = NetworkBuilder('internal')
for model in LIF_models:
params = LIF_models[model].copy()
positions = positions_columinar(N=LIF_models[model]['N'],
center=[0, 10.0, 0],
max_radius=50.0,
height=200.0)
net.add_nodes(x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
**params)
net.add_edges(source={'ei': 'e'},
target={'ei': 'e'},
connection_rule=random_connections,
connection_params={'p': eps},
syn_weight=200,
delay=D,
dynamics_params='ExcToExc.json',
model_template='static_synapse')
net.add_edges(source={'ei': 'e'},
target={'ei': 'i'},
connection_rule=random_connections,
connection_params={'p': eps},
syn_weight=300,
delay=D,
dynamics_params='ExcToInh.json',
model_template='static_synapse')
net.add_edges(source={'ei': 'i'},
target={'ei': 'e'},
connection_rule=random_connections,
connection_params={'p': eps},
syn_weight=-550,
delay=D,
dynamics_params='InhToExc.json',
model_template='static_synapse')
net.add_edges(source={'ei': 'i'},
target={'ei': 'i'},
connection_rule=random_connections,
connection_params={'p': eps},
syn_weight=-300,
delay=D,
dynamics_params='InhToInh.json',
model_template='static_synapse')
net.build()
net.save(output_dir='network')
# net.save_edges(edges_file_name='edges.h5', edge_types_file_name='edge_types.csv', output_dir='network')
# net.save_nodes(nodes_file_name='nodes.h5', node_types_file_name='node_types.csv', output_dir='network')
return net
'''Customs functions like this must return a list of size N'''
# def cortex_positions(N):
# # codex to create a list/numpy array of N (x, y, z) positions.
# return np.random.rand(N,3)
# Create network of cells
'''Once we have a network, we can add nodes (i.e. cells) by calling the add_nodes() method. Here, we have 100 cell. all of
which are of the same type, but with different locations and y-axis rotations. The positions of each cell is defined by the
columinar built-in method, which will random place our cells in a column. The rotation_angle_yaxis is similarly defined by
a built-in function that randomly assigns each cell a given y angle.'''
N = 10
cortex.add_nodes(
N=N, # number of cells
pop_name='Scnn1a',
positions=positions_columinar(
N=N, center=[0, 50.0, 0], max_radius=30.0,
height=100.0), # position cells within column
# positions = cortex_positions(N) # custom positions function
rotation_angle_yaxis=xiter_random(N=N, min_x=0.0, max_x=2 * np.pi),
rotation_angle_zaxis=
3.646878266, # note here that z rotations are all the same, but they could be different by using the function above
potental='exc', # indicate that it is an excitatory type cell (optional)
model_type=
'biophysical', # used by the simulator to indicate that we are using a biophysical cell.
dynamics_params=
'472363762_fit.json', # model parameters (file will be downloaded from the Allen Cell Types Database)
morphology=
'Scnn1a_473845048_m.swc', # - Model morphology (file will be downloaded from the Allen Cell Types Database)
model_processing=
'aibs_perisomatic', # - a custom function used by the simulator to load the model into NEURON using Allen Cell-Types files for perisomatic models
model_template='ctdb:Biophys1.hoc'