def build_input_network(net, output_dir='network/source_input'):
def select_source_cells(sources, target, N_syn=10):
""" Note here that "sources" are given (not "source"). So the iterations occur through every target
with all sources as potential inputs. Faster than before and better if will have common rules.
"""
target_id = target.node_id
source_ids = [s.node_id for s in sources]
nsyns_ret = [N_syn] * len(source_ids)
return nsyns_ret
filter_models = {
'inputFilter': {
'N': 25,
'ei': 'e',
'pop_name': 'input_filter',
'model_type': 'virtual'
}
}
inputNetwork = NetworkBuilder("inputNetwork")
inputNetwork.add_nodes(**filter_models['inputFilter'])
inputNetwork.add_edges(target=net.nodes(pop_name='Scnn1a'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.0007,
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
inputNetwork.add_edges(target=net.nodes(pop_name='LIF_exc'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.07,
delay=2.0,
dynamics_params='instanteneousExc.json')
inputNetwork.add_edges(target=net.nodes(pop_name='PV1'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.002,
distance_range=[0.0, 1.0e+20],
target_sections=['basal', 'somatic'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
inputNetwork.add_edges(target=net.nodes(pop_name='LIF_inh'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.01,
delay=2.0,
dynamics_params='instanteneousExc.json')
inputNetwork.build()
inputNetwork.save(output_dir=output_dir)
def build_hippocampus():
net = NetworkBuilder('CA3')
net.add_nodes(N=1,
pop_name='CA3e',
model_type='biophysical',
model_template='hoc:Spikingcell',
morphology='blank.swc')
inputnet = NetworkBuilder('input')
inputnet.add_nodes(N=1, model_type='virtual', pat='pat1', pop_name='virt')
conn = inputnet.add_edges(
target=net.nodes(pop_name='CA3e'),
source={'pat': 'pat1'},
connection_rule=1,
dynamics_params='stsp.json',
model_template='Exp2Syn1_STSP',
delay=0,
syn_weight=.5,
target_sections=['soma'], # target soma
distance_range=[0.0, 300])
conn.add_properties(['sec_id', 'sec_x'],
rule=(0, 0.9),
dtypes=[np.int32, np.float])
net.build()
net.save_nodes(output_dir='network')
net.save_edges(output_dir='network')
inputnet.build()
inputnet.save(output_dir='network')
net1.add_nodes(N=1,
pop_name='hco2',
cell_name='HCOCell2',
model_type='biophysical',
model_template='hoc:HCOcell',
morphology='blank.swc')
net1.build()
net1.save_nodes(output_dir='network')
net1.add_edges(source={'pop_name': 'hco1'},
target={'pop_name': 'hco2'},
connection_rule=1,
syn_weight=40.0e-03,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=99999999)
net1.add_edges(source={'pop_name': 'hco2'},
target={'pop_name': 'hco1'},
connection_rule=1,
syn_weight=40.0e-03,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=99999999)
return np.random.randint(nsyn_min, nsyn_max)
"""
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=100,
pop_name='tON',
potential='exc',
level_of_detail='filter')
cortex = NetworkBuilder('mcortex')
cortex.import_nodes(nodes_file_name='network/mcortex_nodes.h5',
node_types_file_name='network/mcortex_node_types.csv')
thalamus.add_edges(source=thalamus.nodes(),
target=cortex.nodes(pop_name='Scnn1a'),
connection_rule=connect_random,
connection_params={
'nsyn_min': 0,
'nsyn_max': 12
},
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
thalamus.build()
thalamus.save_nodes(output_dir='network')
thalamus.save_edges(output_dir='network')
pop_name=name,
location='VisL4',
**cell_props)
'''
net.build()
net.save_nodes('tmp_nodes.h5', 'tmp_node_types.csv')
exit()
'''
cparameters = {'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}
net.add_edges(sources={'ei': 'i'}, targets={'ei': 'i', 'level_of_detail': 'biophysical'},
func=distance_connection_handler, func_params=cparameters,
weight_max=0.0002,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='GABA_InhToInh.json',
set_params_function='exp2syn')
net.add_edges(sources={'ei': 'i'}, targets={'ei': 'i', 'level_of_detail': 'intfire'},
func=distance_connection_handler,
func_params=cparameters,
weight_max=0.00225,
weight_function='wmax',
delay=2.0,
params_file='instanteneousInh.json',
set_params_function='exp2syn')
cparameters = {'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}
morphology='blank.swc'
)
net1.add_nodes(N=1,
cell_name='HCOCell2',
model_type='biophysical',
model_template='hoc:HCOcell',
morphology='blank.swc'
)
net1.add_edges(source={'cell_name': 'HCOCell1'}, target={'cell_name':'HCOCell2'},
connection_rule=1,
syn_weight=40.0e-02,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=[0,999])
net1.add_edges(source={'cell_name': 'HCOCell2'}, target={'cell_name':'HCOCell1'},
connection_rule=1,
syn_weight=40.0e-02,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=[0,999])
net1.build()
net1.save_nodes(output_dir='network')
#syn_weight = np.random.lognormal(mean=mu,sigma=sigma)
syn_weight = mu
return syn_weight,0,0.5
def one_to_one(source,target):
tmp_syn = 1
return tmp_syn
# Add connections -----------------------------------------
conn = net.add_edges(source=net.nodes(pop_name='PUDaff'), target=net.nodes(pop_name='INmminus'),
connection_rule=one_to_one,
delay=0.5,
syn_weight = 0.07,
target_sections=['somatic'],
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
conn = net.add_edges(source=net.nodes(pop_name='PUDaff'), target=net.nodes(pop_name='INmplus'),
connection_rule=one_to_one,
delay=0.5,
syn_weight = 0.044,
target_sections=['somatic'],
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
conn = net.add_edges(source=net.nodes(pop_name='PUDaff'), target=net.nodes(pop_name='IND'),
connection_rule=one_to_one,
def build_lgn():
def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
X = np.random.uniform(x0, x1, N)
Y = np.random.uniform(y0, y1, N)
return np.column_stack((X, Y))
def select_source_cells(src_cells, trg_cell, n_syns):
if trg_cell['tuning_angle'] is not None:
synapses = [n_syns if src['pop_name'] == 'tON' or src['pop_name'] == 'tOFF' else 0 for src in src_cells]
else:
synapses = [n_syns if src['pop_name'] == 'tONOFF' else 0 for src in src_cells]
return synapses
if not os.path.exists('output'):
os.makedirs('output')
LGN = NetworkBuilder("LGN")
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tON',
ei='e')
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tOFF',
ei='e')
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tONOFF',
ei='e')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Rorb'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Nr5a1'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Scnn1a'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=4e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'PV1'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.0001,
weight_function='wmax',
distance_range=[0.0, 1.0e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'PV2'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=9e-05,
weight_function='wmax',
distance_range=[0.0, 1.0e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'LIF_exc'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.0045,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'LIF_inh'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.002,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
VL4.build()
LGN.save_nodes(nodes_file_name='output/lgn_nodes.h5', node_types_file_name='output/lgn_node_types.csv')
VL4.save_edges(edges_file_name='output/lgn_v1_edges.h5', edge_types_file_name='output/lgn_v1_edge_types.csv')
assert(os.path.exists('output/lgn_node_types.csv'))
node_types_csv = pd.read_csv('output/lgn_node_types.csv', sep=' ')
assert(len(node_types_csv) == 3)
assert(set(node_types_csv.columns) == {'node_type_id', 'location', 'ei', 'level_of_detail', 'pop_name'})
assert(os.path.exists('output/lgn_nodes.h5'))
nodes_h5 = h5py.File('output/lgn_nodes.h5')
assert(len(nodes_h5['/nodes/node_gid']) == 9000)
assert(len(nodes_h5['/nodes/node_type_id']) == 9000)
assert(len(nodes_h5['/nodes/node_group']) == 9000)
assert(len(nodes_h5['/nodes/node_group_index']) == 9000)
assert(set(nodes_h5['/nodes/0'].keys()) == {'positions'})
assert(len(nodes_h5['/nodes/0/positions']) == 9000)
assert(os.path.exists('output/lgn_v1_edge_types.csv'))
edge_types_csv = pd.read_csv('output/lgn_v1_edge_types.csv', sep=' ')
assert(len(edge_types_csv) == 7)
assert(set(edge_types_csv.columns) == {'weight_max', 'edge_type_id', 'target_query', 'params_file',
'set_params_function', 'delay', 'target_sections', 'weight_function',
'source_query', 'distance_range'})
assert(os.path.exists('output/lgn_v1_edges.h5'))
edges_h5 = h5py.File('output/lgn_v1_edges.h5')
assert(len(edges_h5['/edges/index_pointer']) == 14+1)
assert(len(edges_h5['/edges/target_gid']) == 6000*14)
assert(len(edges_h5['/edges/source_gid']) == 6000*14)
assert(len(edges_h5['/edges/0/nsyns']) == 6000*14)
dynamics_params='IntFire1_exc_point.json')
net.add_nodes(N=100, pop_name='LIF_inh', location='L4', ei='i',
positions=positions_columinar(N=100, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),
model_type='point_process',
model_template='nest:iaf_psc_alpha',
dynamics_params='IntFire1_inh_point.json')
"""Create edges"""
## E-to-E connections
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Scnn1a'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=5.0,
delay=2.0,
dynamics_params='ExcToExc.json',
model_template='static_synapse')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},
syn_weight=10.0,
delay=2.0,
dynamics_params='instanteneousExc.json',
model_template='static_synapse')
### Generating I-to-I connections
net.add_edges(source={'ei': 'i'}, target={'pop_name': 'PV'},
(218.04, -327.333, -635.593)],
model_type='point_process',
model_template='nrn:IntFire1',
dynamics_params='IntFire1_inh_1.json')
# Step 2: We want to connect our network. Just like how we have node-types concept we group our connections into
# "edge-types" that share rules and properties
net.add_edges(
source={
'ei': 'i'
}, # For our synaptic source cells we select all inhibitory cells (ei==i), incl. both biophys and point
target={
'ei': 'i',
'model_type': 'biophysical'
}, # For our synaptic target we select all inhibitory biophysically detailed cells
connection_rule=5, # All matching source/target pairs will have
syn_weight=0.0002, # synaptic weight
target_sections=['somatic',
'basal'], # Gives the simulator the target sections and
distance_range=[0.0,
1e+20], # distances (from soma) when creating connections
delay=2.0,
dynamics_params='GABA_InhToInh.json',
model_template='exp2syn')
net.add_edges(
source={'ei': 'i'},
target={
'ei': 'i',
'model_type': 'point_process'
},
def build_cortical_network(output_dir='network/recurrent_network'):
def distance_connection_handler(source, target, d_max, nsyn_min, nsyn_max):
""" Connect cells that are less than d_max apart with a random number of synapses in the
interval [nsyn_min, nsyn_max)
"""
sid = source['node_id'] # Get source id
tid = target['node_id'] # Get target id
# Avoid self-connections.
if (sid == tid):
return None
# first calculate euclidean distance between cells
src_positions = np.array([source['x'], source['y'], source['z']])
trg_positions = np.array([target['x'], target['y'], target['z']])
separation = np.sqrt(np.sum(src_positions - trg_positions)**2)
# drop the connection if nodes too far apart
if separation >= d_max:
return None
# Add the number of synapses for every connection.
tmp_nsyn = random.randint(nsyn_min, nsyn_max)
return tmp_nsyn
#### Step 1: Figure out what types, and number of, different cells to use in our network ####
# Number of cell models desired
N_Scnn1a = 2
N_PV1 = 2
N_LIF_exc = 2
N_LIF_inh = 2
# Define all the cell models in a dictionary (note dictionaries within a dictionary)
biophysical_models = {
'Scnn1a': {
'N': N_Scnn1a,
'ei': 'e',
'pop_name': 'Scnn1a',
'model_type': 'biophysical',
'model_template': 'ctdb:Biophys1.hoc',
'model_processing': 'aibs_perisomatic',
'morphology_file':
'Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
'dynamics_params': '472363762_fit.json',
'rotation_angle_zaxis': -3.646878266
},
'PV1': {
'N': N_PV1,
'ei': 'i',
'pop_name': 'PV1',
'model_type': 'biophysical',
'model_template': 'ctdb:Biophys1.hoc',
'model_processing': 'aibs_perisomatic',
'dynamics_params': '472912177_fit.json',
'morphology_file':
'Pvalb-IRES-Cre_Ai14_IVSCC_-176847.04.02.01_470522102_m.swc',
'rotation_angle_zaxis': -2.539551891
}
}
# Define all the cell models in a dictionary.
LIF_models = {
'LIF_exc': {
'N': N_LIF_exc,
'ei': 'e',
'pop_name': 'LIF_exc',
'model_type': 'point_process',
'model_template': 'nrn:IntFire1',
'dynamics_params': 'IntFire1_exc_1.json'
},
'LIF_inh': {
'N': N_LIF_inh,
'ei': 'i',
'pop_name': 'LIF_inh',
'model_type': 'point_process',
'model_template': 'nrn:IntFire1',
'dynamics_params': 'IntFire1_inh_1.json'
}
}
#### Step 2: Create NetworkBuidler object to build nodes and edges ####
net = NetworkBuilder('Cortical')
#### Step 3: Used add_nodes() method to add all our cells/cell-types
for model in biophysical_models:
# Build our biophysical cells
params = biophysical_models[model]
n_cells = params.pop('N')
# We'll randomly assign positions
positions = generate_random_positions(n_cells)
# Use add_nodes to create a set of N cells for each cell-type
net.add_nodes(
N=n_cells, # Specify the numer of cells belonging to this set of nodes
x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
rotation_angle_yaxis=np.random.uniform(0.0, 2 * np.pi, n_cells),
# The other parameters are shared by all cells of this set in the dictionary
**params) # python shortcut for unrolling a dictionary
for model in LIF_models:
# Same thing as above but for our LIF type cells
params = LIF_models[model].copy()
# Number of cells for this model type
n_cells = params.pop('N')
# Precacluate positions, rotation angles for each N neurons in the population
positions = generate_random_positions(n_cells)
# Adds node populations
net.add_nodes(N=n_cells,
x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
rotation_angle_yaxis=np.random.uniform(
0.0, 2 * np.pi, n_cells),
**params)
#### Step 4: Used add_edges() to set our connections between cells ####
cparameters = {
'd_max':
160.0, # Maximum separation between nodes where connection allowed
'nsyn_min': 3, # If connection exist, minimum number of synapses
'nsyn_max': 7
} # If connection exist, maximum number of synapses
net.add_edges(
source={
'ei': 'i'
}, # Select all inhibitory cells to apply this connection rule too
target={
'ei': 'i',
'model_type': 'biophysical'
}, # for the target cells we will use inhibitory biophysical cells
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.03,
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='GABA_InhToInh.json',
model_template='exp2syn')
# inhibitory --> point-inhibitory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'i',
'model_type': 'point_process'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.3,
delay=2.0,
dynamics_params='instanteneousInh.json')
# inhibiotry --> biophysical-excitatory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'e',
'model_type': 'biophysical'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.3,
distance_range=[0.0, 50.0],
target_sections=['somatic', 'basal', 'apical'],
delay=2.0,
dynamics_params='GABA_InhToExc.json',
model_template='exp2syn')
# inhibitory --> point-excitatory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'e',
'model_type': 'point_process'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.4,
delay=2.0,
dynamics_params='instanteneousInh.json')
# excitatory --> PV1 cells
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'PV1'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
# excitatory --> LIF_inh
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'LIF_inh'},
connection_rule=distance_connection_handler,
connection_params=cparameters,
syn_weight=0.2,
delay=2.0,
dynamics_params='instanteneousExc.json')
# excitatory --> Scnn1a
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'Scnn1a'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
# excitatory --> LIF_exc
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'LIF_exc'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
delay=2.0,
dynamics_params='instanteneousExc.json')
#### Step 5: Build and save the network ####
net.build()
net.save(output_dir=output_dir)
return net
def recurrent_connections_low2(src_cells, trg_cell, n_syns):
synapses = [
n_syns * (np.random.random() > .1) for i in range(len(src_cells))
]
return synapses
net_pre.add_edges(source=net_pre.nodes(),
target=net_post.nodes(pop_name='Exc'),
iterator='all_to_one',
connection_rule=recurrent_connections_low,
connection_params={'n_syns': 1},
syn_weight=5,
weight_function='wmax',
delay=0.0,
dynamics_params='instanteneousExc.json')
'''
'''
'''
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'point_process'},
iterator='all_to_one',
connection_rule=recurrent_connections,
connection_params={'n_syns': 10},
syn_weight=0.01,
weight_function='wmax',
delay=0.0,
dynamics_params='instanteneousInh.json')
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
},
def build_lgn():
def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
X = np.random.uniform(x0, x1, N)
Y = np.random.uniform(y0, y1, N)
return np.column_stack((X, Y))
def select_source_cells(src_cell, trg_cell, n_syns_exc, n_syns_inh):
#if trg_cell['tuning_angle'] is not None:
# synapses = [n_syns if src['pop_name'] == 'tON' or src['pop_name'] == 'tOFF' else 0 for src in src_cells]
#else:
# synapses = [n_syns if src['pop_name'] == 'tONOFF' else 0 for src in src_cells]
# will always give synapse number depending on type
if n_syns_exc > 0:
n_synapses = n_syns_exc
if n_syns_inh > 0:
n_synapses = n_syns_inh
return n_synapses
# varible number of synapses
#return np.random.randint(n_syns_min, n_syns_max)
#return synapses
if not os.path.exists('output'):
os.makedirs('output')
LGN = NetworkBuilder("LGN")
LGN.add_nodes(N=1000,
positions=generate_positions(1000),
location='LGN',
level_of_detail='filter',
pop_name='ExcIN',
ei='e')
LGN.add_nodes(N=1,
positions=generate_positions(1),
location='LGN',
level_of_detail='filter',
pop_name='InhIN',
ei='i')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
cm = VL4.add_edges(source=LGN.nodes(ei='e'), #target={'pop_name': 'DG_GC'},
connection_rule=select_source_cells,
connection_params={'n_syns_exc': 1, 'n_syns_inh': 0},
weight_max=10e-03,
weight_function='wmax',
distance_range=[1, 1e+20],
target_sections=['apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.build()
LGN.save_nodes(nodes_file_name='output/lgn_nodes.h5', node_types_file_name='output/lgn_node_types.csv')
VL4.save_edges(edges_file_name='output/lgn_v1_edges.h5', edge_types_file_name='output/lgn_v1_edge_types.csv')
net.add_nodes(
N=1,
level='low',
ei='e',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='473863035_fit.json',
morphology='Nr5a1-Cre_Ai14_IVSCC_-169250.03.02.01_471087815_m.swc')
# Create synapse from the high-level cell to the low-level cell (0 --> 1)
net.add_edges(source={'level': 'high'},
target={'level': 'low'},
connection_rule=5,
syn_weight=12.0e-03,
target_sections=['somatic'],
distance_range=[0.0, 300.0],
delay=1.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
# build and save the network.
net.build()
net.save(output_dir='network')
# An external network with 1 (excitatory virtual) cell that will connect to the internal excitatory cell to drive
# it with the spikes inputs
inputs = NetworkBuilder("external")
inputs.add_nodes(N=1, model_type='virtual')
inputs.add_edges(
source=inputs.nodes(),
def build_tw():
if not os.path.exists('output'):
os.makedirs('output')
TW = NetworkBuilder("TW")
TW.add_nodes(N=3000, pop_name='TW', ei='e', location='TW', level_of_detail='filter')
# Save cells.csv and cell_types.csv
TW.save_nodes(nodes_file_name='output/tw_nodes.h5', node_types_file_name='output/tw_node_types.csv')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Rorb'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00015, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Scnn1a'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00019, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Nr5a1'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00019, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'PV1'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.0022, 'weight_function': 'wmax', 'distance_range': [0.0, 1.0e+20],
'target_sections': ['somatic', 'basal'], 'delay': 2.0, 'params_file': 'AMPA_ExcToInh.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'PV2'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.0013, 'weight_function': 'wmax', 'distance_range': [0.0, 1.0e+20],
'target_sections': ['somatic', 'basal'], 'delay': 2.0, 'params_file': 'AMPA_ExcToInh.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'LIF_exc'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.015, 'weight_function': 'wmax', 'delay': 2.0,
'params_file': 'instanteneousExc.json', 'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'LIF_inh'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.05, 'weight_function': 'wmax', 'delay': 2.0,
'params_file': 'instanteneousExc.json', 'set_params_function': 'exp2syn'}))
VL4.build()
VL4.save_edges(edges_file_name='output/tw_v1_edges.h5', edge_types_file_name='output/tw_v1_edge_types.csv')
max(zcoords) - min(zcoords))
print "> L4 mean center:", str(l4_mean)
cparams = {
'lgn_mean': lgn_mean,
'lgn_dim': lgn_dim,
'l4_mean': l4_mean,
'l4_dim': l4_dim,
'N_syn': 30
}
v1_net.add_edges(sources=lgn_net.nodes(),
targets={'name': 'Rorb'},
iterator='all_to_one',
func=select_source_cells,
func_params=cparams,
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
v1_net.add_edges(sources=lgn_net.nodes(),
targets={'name': 'Nr5a1'},
iterator='all_to_one',
func=select_source_cells,
func_params=cparams,
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
# EXCITATORY POPULATION, CONNECTIONS
# gEE reccurent excitation
net.add_edges(source={
'ei': 'e',
'model_type': 'biophysical'
},
target={
'ei': 'e',
'model_type': 'biophysical'
},
connection_rule=connection_phi_same_layer,
connection_params={
'nedges': 100,
'nsyns_min': 2,
'nsyns_max': 5,
'divergence': 50
},
iterator='one_to_all',
syn_weight=0.00013,
weight_function='wmax',
distance_range=[0.0, 150],
target_sections=['apical', 'basal'],
delay=0.8,
model_template='exp2syn',
dynamics_params='AMPA_ExcToExc_GC_GC.json')
# additional reccurent connections
net.add_edges(source={
'ei': 'e',
print("connecting {} cell {} to {} cell {}".format(
source_name, sid, target_name, tid))
tmp_nsyn = 1
else:
return None
return tmp_nsyn
# Add connections
# Blad afferent --> INd
net.add_edges(source=net.nodes(pop_name='Bladaff'),
target=net.nodes(pop_name='IND'),
connection_rule=one_to_one,
syn_weight=12.0e-03,
target_sections=['somatic'],
delay=2.0,
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
# EUS afferent --> INd
net.add_edges(source=net.nodes(pop_name='EUSaff'),
target=net.nodes(pop_name='IND'),
connection_rule=one_to_one,
syn_weight=12.0e-03,
target_sections=['somatic'],
delay=2.0,
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
def build_l4():
net = NetworkBuilder("V1/L4")
net.add_nodes(N=2, pop_name='Scnn1a',
positions=[(28.753, -364.868, -161.705), (48.753, -344.868, -141.705)],
tuning_angle=[0.0, 25.0],
rotation_angle_yaxis=[3.55501, 3.55501],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
rotation_angle_zaxis=-3.646878266,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='Rorb',
positions=[(241.092, -349.263, 146.916), (201.092, -399.263, 126.916)],
tuning_angle=[50.0, 75.0],
rotation_angle_yaxis=[3.50934, 3.50934],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='473863510_fit.json',
morphology_file='Rorb-IRES2-Cre-D_Ai14_IVSCC_-168053.05.01.01_325404214_m.swc',
rotation_angle_zaxis=-4.159763785,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='Nr5a1',
positions=[(320.498, -351.259, 20.273), (310.498, -371.259, 10.273)],
tuning_angle=[100.0, 125.0],
rotation_angle_yaxis=[0.72202, 0.72202],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='473863035_fit.json',
morphology_file='Nr5a1-Cre_Ai14_IVSCC_-169250.03.02.01_471087815_m.swc',
rotation_angle_zaxis=-2.639275277,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='PV1',
positions=[(122.373, -352.417, -216.748), (102.373, -342.417, -206.748)],
tuning_angle=['NA', 'NA'],
rotation_angle_yaxis=[2.92043, 2.92043],
location='VisL4',
ei='i',
level_of_detail='biophysical',
params_file='472912177_fit.json',
morphology_file='Pvalb-IRES-Cre_Ai14_IVSCC_-176847.04.02.01_470522102_m.swc',
rotation_angle_zaxis=-2.539551891,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='PV2',
positions=[(350.321, -372.535, -18.282), (360.321, -371.535, -12.282)],
tuning_angle=['NA', 'NA'],
rotation_angle_yaxis=[5.043336, 5.043336],
location='VisL4',
ei='i',
level_of_detail='biophysical',
params_file='473862421_fit.json',
morphology_file='Pvalb-IRES-Cre_Ai14_IVSCC_-169125.03.01.01_469628681_m.swc',
rotation_angle_zaxis=-3.684439949,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='LIF_exc',
positions=[(-243.04, -342.352, -665.666), (-233.04, -332.352, -675.666)],
tuning_angle=['NA', 'NA'],
#rotation_angle_yaxis=[5.11801, 5.11801],
location='VisL4',
ei='e',
level_of_detail='intfire',
params_file='IntFire1_exc_1.json',
set_params_function='IntFire1')
net.add_nodes(N=2, pop_name='LIF_inh',
positions=[(211.04, -321.333, -631.593), (218.04, -327.333, -635.593)],
tuning_angle=[150.0, 175.0],
#rotation_angle_yaxis=[4.566091, 4.566091],
location='VisL4',
ei='i',
level_of_detail='intfire',
params_file='IntFire1_inh_1.json',
set_params_function='IntFire1')
print("Setting connections...")
print("Generating I-to-I connections.")
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'level_of_detail': 'biophysical'},
connection_rule=5,
weight_max=0.0002,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='GABA_InhToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'level_of_detail': 'intfire'},
connection_rule=5,
weight_max=0.00225,
weight_function='wmax',
delay=2.0,
params_file='instanteneousInh.json',
set_params_function='exp2syn')
print("Generating I-to-E connections.")
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'level_of_detail': 'biophysical'},
connection_rule=lambda trg, src: 5,
weight_max=0.00018,
weight_function='wmax',
distance_range=[0.0, 50.0],
target_sections=['somatic', 'basal', 'apical'],
delay=2.0,
params_file='GABA_InhToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'level_of_detail': 'intfire'},
connection_rule=5,
weight_max=0.009,
weight_function='wmax',
delay=2.0,
params_file='instanteneousInh.json',
set_params_function='exp2syn')
print("Generating E-to-I connections.")
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV1'},
connection_rule=5,
weight_max=0.00035,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV2'},
connection_rule=5,
weight_max=0.00027,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'},
connection_rule=5,
weight_max=0.0043,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
print("Generating E-to-E connections.")
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Scnn1a'},
connection_rule=5,
weight_max=6.4e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Rorb'},
connection_rule=5,
weight_max=5.5e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Nr5a1'},
connection_rule=5,
weight_max=7.2e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},
connection_rule=5,
weight_max=0.0019,
weight_function='gaussianLL',
weight_sigma=50.0,
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
net.build()
net.save_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
net.save_edges(edges_file_name='output/v1_v1_edges.h5', edge_types_file_name='output/v1_v1_edge_types.csv')
assert(os.path.exists('output/v1_node_types.csv'))
node_types_csv = pd.read_csv('output/v1_node_types.csv', sep=' ')
assert(len(node_types_csv) == 7)
assert(set(node_types_csv.columns) == {'node_type_id', 'location', 'ei', 'level_of_detail', 'params_file',
'pop_name', 'set_params_function', 'rotation_angle_zaxis',
'morphology_file'})
assert(os.path.exists('output/v1_nodes.h5'))
nodes_h5 = h5py.File('output/v1_nodes.h5')
assert(len(nodes_h5['/nodes/node_gid']) == 14)
assert(len(nodes_h5['/nodes/node_type_id']) == 14)
assert(len(nodes_h5['/nodes/node_group']) == 14)
assert(len(nodes_h5['/nodes/node_group_index']) == 14)
assert(set(nodes_h5['/nodes/0'].keys()) == {'positions', 'rotation_angle_yaxis', 'tuning_angle'})
assert(len(nodes_h5['/nodes/0/positions']) == 10)
assert(len(nodes_h5['/nodes/1/positions']) == 4)
assert(os.path.exists('output/v1_v1_edge_types.csv'))
edge_types_csv = pd.read_csv('output/v1_v1_edge_types.csv', sep=' ')
assert(len(edge_types_csv) == 11)
assert(set(edge_types_csv.columns) == {'weight_max', 'edge_type_id', 'target_query', 'params_file',
'set_params_function', 'delay', 'target_sections', 'weight_function',
'weight_sigma', 'source_query', 'distance_range'})
assert(os.path.exists('output/v1_v1_edges.h5'))
edges_h5 = h5py.File('output/v1_v1_edges.h5')
assert(len(edges_h5['/edges/index_pointer']) == 14+1)
assert(len(edges_h5['/edges/target_gid']) == 14*14)
assert(len(edges_h5['/edges/source_gid']) == 14*14)
assert(len(edges_h5['/edges/0/nsyns']) == 14*14)
return net
rotation_angle_yaxis=0,
rotation_angle_zaxis=-3.011895, # Note that the y-axis rotation is differnt for each cell (ie. given a list of size N), but with z-axis rotation all cells have the same value
model_type='biophysical', # The type of cell we are using
model_template='ctdb:Biophys1.hoc', # Tells the simulator that when building cells models use a hoc_template specially created for parsing Allen Cell-types file models. Value would be different if we were using NeuronML or different model files
model_processing='aibs_allactive_ani_directed', # further instructions for how to processes a cell model. In this case aibs_perisomatic is a built-in directive to cut the axon in a specific way
dynamics_params='optim_param_571654895.json', # Name of file (downloaded from Allen Cell-Types) used to set model parameters and channels
morphology='571654895.swc'), # Name of morphology file downloaded
# Step 2: We want to connect our network. Just like how we have node-types concept we group our connections into
# "edge-types" that share rules and properties
net.add_edges(source={'ei': 'e'}, target={'pop_name': ''},
connection_rule=5,
syn_weight=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.build()
net.save(output_dir='network')
def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
X = np.random.uniform(x0, x1, N)
Y = np.random.uniform(y0, y1, N)
return np.column_stack((X, Y))
def select_source_cells(src_cells, trg_cell, n_syns):
model_type='biophysical', # The type of cell we are using
model_template=
'ctdb:Biophys1.hoc', # Tells the simulator that when building cells models use a hoc_template specially created for parsing Allen Cell-types file models. Value would be different if we were using NeuronML or different model files
model_processing=
'aibs_allactive_ani_directed', # further instructions for how to processes a cell model. In this case aibs_perisomatic is a built-in directive to cut the axon in a specific way
dynamics_params=
'hof_param_CELLID_0.json', # Name of file (downloaded from Allen Cell-Types) used to set model parameters and channels
morphology='CELLID.swc'), # Name of morphology file downloaded
# Step 2: We want to connect our network. Just like how we have node-types concept we group our connections into
# "edge-types" that share rules and properties
net.add_edges(source={'ei': 'i'},
target={'pop_name': 'POPNAME'},
connection_rule=5,
syn_weight=6.4e-05,
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.build()
net.save(output_dir='network')
def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
X = np.random.uniform(x0, x1, N)
Y = np.random.uniform(y0, y1, N)
return np.column_stack((X, Y))
def select_source_cells(src_cells, trg_cell, n_syns):
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},
weight_max=6.4e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='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},
weight_max=0.0019,
weight_function='gaussianLL',
weight_sigma=50.0,
delay=2.0,
params_file='instanteneousExc.json',
def connect_random(source, target, nsyn_min=0, nsyn_max=10, distribution=None):
return np.random.randint(nsyn_min, nsyn_max)
"""
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=100,
pop_name='tON',
potential='exc',
level_of_detail='filter')
cortex = NetworkBuilder('mcortex')
cortex.import_nodes(nodes_file_name='network/mcortex_nodes.h5',
node_types_file_name='network/mcortex_node_types.csv')
thalamus.add_edges(source=thalamus.nodes(),
target=cortex.nodes(),
connection_rule=connect_random,
connection_params={
'nsyn_min': 0,
'nsyn_max': 12
},
syn_weight=1.0e-04,
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
thalamus.build()
thalamus.save_nodes(output_dir='network')
thalamus.save_edges(output_dir='network')
potental='exc',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a.swc',
set_params_function='Biophys1')
cortex.build()
cortex.save_nodes(output_dir='network')
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=10,
pop_name='tON',
potential='exc',
level_of_detail='filter')
thalamus.add_edges(source={'pop_name': 'tON'},
target=cortex.nodes(),
connection_rule=5,
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
thalamus.build()
thalamus.save_nodes(output_dir='network')
thalamus.save_edges(output_dir='network')
#thalamus.save_edges(edges_file_name='thalamus_cortex_edges.h5', edge_types_file_name='thalamus_cortex_edge_types.h5')
def build_ext5_nodes():
if not os.path.exists('network'):
os.makedirs('network')
EXT = NetworkBuilder("EXT")
# need 5 cells to stimulate at 5 different frequencies
EXT.add_nodes(N=5,
pop_name='EXT',
model_type='virtual',
firing_rate=firing_rate)
# Save cells.csv and cell_types.csv
EXT.save_nodes(nodes_file_name='network/ext_nodes.h5',
node_types_file_name='network/ext_node_types.csv')
net = NetworkBuilder('slice')
net.import_nodes(nodes_file_name='network/slice_nodes.h5',
node_types_file_name='network/slice_node_types.csv')
net.add_edges(source=EXT.nodes(firing_rate=10),
target=net.nodes(firing_rate=10, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=20),
target=net.nodes(firing_rate=20, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=50),
target=net.nodes(firing_rate=50, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=100),
target=net.nodes(firing_rate=100, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=200),
target=net.nodes(firing_rate=200, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=10),
target=net.nodes(firing_rate=10, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=20),
target=net.nodes(firing_rate=20, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=50),
target=net.nodes(firing_rate=50, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=100),
target=net.nodes(firing_rate=100, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=200),
target=net.nodes(firing_rate=200, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.build()
net.save_edges(edges_file_name='network/ext_to_slice_edges.h5',
edge_types_file_name='network/ext_to_slice_edge_types.csv')
If done this way the function will need to be imported in the run script, consider refactoring?
"""
#syn_weight = np.random.lognormal(mean=mu,sigma=sigma)
syn_weight = mu
return syn_weight,0,0.5
# Add connections -----------------------------------------
# Blad afferent --> INd (Grill et al. 2016)
conn = net.add_edges(source=net.nodes(pop_name='Bladaff'), target=net.nodes(pop_name='IND'),
connection_rule=percent_connector,
connection_params={'percent':100.0},
target_sections=['somatic'],
delay=2.0,
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
conn.add_properties(names=['syn_weight', 'sec_id', 'sec_x'],
rule=conn_props,
rule_params={'mu':10.0e-3,'sigma':1},
dtypes=[np.float, np.int32, np.float])
# Blad afferent --> Hypogastric (Hou et al. 2014)
# conn = net.add_edges(source=net.nodes(pop_name='Bladaff'), target=net.nodes(pop_name='Hypo'),
# connection_rule=percent_connector,
# connection_params={'percent':10.0},
# target_sections=['somatic'],
# delay=2.0,
from bmtk.builder.networks import NetworkBuilder
def crule(src, trg):
# print src.node_id, trg.node_id
return 2
net1 = NetworkBuilder('NET1')
net1.add_nodes(N=100,
position=[(0.0, 1.0, -1.0)] * 100,
cell_type='Scnna1',
ei='e')
net1.add_edges(source={'ei': 'e'}, target={'ei': 'e'}, connection_rule=5)
net1.build()
net2 = NetworkBuilder('NET2')
net2.add_nodes(N=10, position=[(0.0, 1.0, -1.0)] * 10, cell_type='PV1', ei='i')
net2.add_edges(connection_rule=10)
net2.add_edges(source=net1.nodes(), connection_rule=1)
net2.add_edges(target=net1.nodes(), connection_rule=crule)
net2.build()
#net1.save_edges(output_dir='tmp_output')
net2.save_edges(output_dir='tmp_output')
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')
cortex.build()
cortex.save_nodes(output_dir='network')
cortex.save_edges(output_dir='network')
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.build()
cortex.save_nodes(output_dir='network')
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=10,
pop_name='tON',
potential='exc',
level_of_detail='filter')
thalamus.add_edges(source={'pop_name': 'tON'}, target=cortex.nodes(),
connection_rule=5,
syn_weight=0.001,
delay=2.0,
weight_function='wmax',
target_sections=['basal', 'apical'],
distance_range=[0.0, 150.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
thalamus.build()
thalamus.save_nodes(output_dir='network')
thalamus.save_edges(output_dir='network')