net.input_sources.append(input_sourceI)
net.inputs.append(
Input(id='stimE',
input_source=input_sourceE.id,
population=pE.id,
percentage=100))
net.inputs.append(
Input(id='stimI',
input_source=input_sourceI.id,
population=pI.id,
percentage=100))
####################### Save network in json file #############################
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='SimJoglekar1Network',
network=new_file,
duration=duration,
dt=dt,
seed=1234,
recordTraces={
pE.id: [0, 1],
pI.id: [0, 1]
},
recordSpikes={
def generate(ref='Example6_PyNN', add_inputs=True):
################################################################################
### Build new network
net = Network(id=ref,
notes='Another network for PyNN - work in progress...')
net.parameters = {
'N_scaling': 0.005,
'layer_height': 400,
'width': 100,
'depth': 100,
'input_weight': 0.1
}
cell = Cell(id='CorticalCell', pynn_cell='IF_curr_exp')
cell.parameters = {
'cm': 0.25, # nF
'i_offset': 0.0, # nA
'tau_m': 10.0, # ms
'tau_refrac': 2.0, # ms
'v_reset': -65.0, # mV
'v_rest': -65.0, # mV
'v_thresh': -50.0 # mV
}
net.cells.append(cell)
if add_inputs:
input_cell = Cell(id='InputCell', pynn_cell='SpikeSourcePoisson')
input_cell.parameters = {
'start': 0,
'duration': 10000000000,
'rate': 150
}
net.cells.append(input_cell)
e_syn = Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_exp',
parameters={'tau_syn': 0.5})
net.synapses.append(e_syn)
i_syn = Synapse(id='gaba',
pynn_receptor_type='inhibitory',
pynn_synapse_type='curr_exp',
parameters={'tau_syn': 0.5})
net.synapses.append(i_syn)
N_full = {
'L23': {
'E': 20683,
'I': 5834
},
'L4': {
'E': 21915,
'I': 5479
},
'L5': {
'E': 4850,
'I': 1065
},
'L6': {
'E': 14395,
'I': 2948
}
}
scale = 0.1
pops = []
input_pops = []
pop_dict = {}
layers = ['L23']
layers = ['L23', 'L4', 'L5', 'L6']
for l in layers:
i = 3 - layers.index(l)
r = RectangularRegion(id=l,
x=0,
y=i * net.parameters['layer_height'],
z=0,
width=net.parameters['width'],
height=net.parameters['layer_height'],
depth=net.parameters['depth'])
net.regions.append(r)
for t in ['E', 'I']:
try:
import opencortex.utils.color as occ
if l == 'L23':
if t == 'E': color = occ.L23_PRINCIPAL_CELL
if t == 'I': color = occ.L23_INTERNEURON
if l == 'L4':
if t == 'E': color = occ.L4_PRINCIPAL_CELL
if t == 'I': color = occ.L4_INTERNEURON
if l == 'L5':
if t == 'E': color = occ.L5_PRINCIPAL_CELL
if t == 'I': color = occ.L5_INTERNEURON
if l == 'L6':
if t == 'E': color = occ.L6_PRINCIPAL_CELL
if t == 'I': color = occ.L6_INTERNEURON
except:
color = '.8 0 0' if t == 'E' else '0 0 1'
pop_id = '%s_%s' % (l, t)
pops.append(pop_id)
ref = 'l%s%s' % (l[1:], t.lower())
exec(
ref +
" = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % ref)
exec("net.populations.append(%s)" % ref)
exec("pop_dict['%s'] = %s" % (pop_id, ref))
if add_inputs:
color = '.8 .8 .8'
input_id = '%s_%s_input' % (l, t)
input_pops.append(input_id)
input_ref = 'l%s%s_i' % (l[1:], t.lower())
exec(
input_ref +
" = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" %
input_ref)
exec("net.populations.append(%s)" % input_ref)
#l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':})
#l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id)
#l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id)
#net.populations.append(l23e)
#net.populations.append(l23ei)
#net.populations.append(l23i)
#net.populations.append(l23ii)
conn_probs = [
[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.],
[0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.],
[0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.],
[0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.],
[0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.],
[0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.],
[0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
[0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]
]
if add_inputs:
for p in pops:
proj = Projection(id='proj_input_%s' % p,
presynaptic='%s_input' % p,
postsynaptic=p,
synapse=e_syn.id,
delay=2,
weight='input_weight')
proj.one_to_one_connector = OneToOneConnector()
net.projections.append(proj)
for pre_i in range(len(pops)):
for post_i in range(len(pops)):
pre = pops[pre_i]
post = pops[post_i]
prob = conn_probs[post_i][pre_i] ####### TODO: check!!!!
weight = 1
syn = e_syn
if prob > 0:
if 'I' in pre:
weight = -1
syn = i_syn
proj = Projection(id='proj_%s_%s' % (pre, post),
presynaptic=pre,
postsynaptic=post,
synapse=syn.id,
delay=1,
weight=weight)
proj.random_connectivity = RandomConnectivity(probability=prob)
net.projections.append(proj)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
recordTraces = {}
recordSpikes = {}
from neuromllite.utils import evaluate
for p in pops:
forecast_size = evaluate(pop_dict[p].size, net.parameters)
recordTraces[p] = list(range(min(2, forecast_size)))
recordSpikes[p] = '*'
for ip in input_pops:
recordSpikes[ip] = '*'
sim = Simulation(id='Sim%s' % net.id,
network=new_file,
duration='100',
dt='0.025',
seed=1234,
recordTraces=recordTraces,
recordSpikes=recordSpikes)
sim.to_json_file()
return sim, net
def generate(ref, np2=0, np5=0, nb2=0, nb5=0, recordTraces='*'):
################################################################################
### Build new network
net = Network(id=ref)
net.notes = 'Example: %s...' % ref
net.seed = 7890
net.temperature = 32
net.parameters = {
'np2': np2,
'np5': np5,
'nb2': nb2,
'nb5': nb5,
'offset_curr_l2p': -0.05,
'weight_bkg_l2p': 0.01,
'weight_bkg_l5p': 0.01
}
l2p_cell = Cell(id='CELL_HH_reduced_L2Pyr',
neuroml2_source_file='../CELL_HH_reduced_L2Pyr.cell.nml')
net.cells.append(l2p_cell)
l5p_cell = Cell(id='CELL_HH_reduced_L5Pyr',
neuroml2_source_file='../CELL_HH_reduced_L5Pyr.cell.nml')
net.cells.append(l5p_cell)
l2b_cell = Cell(id='CELL_HH_simple_L2Basket',
neuroml2_source_file='../CELL_HH_simple_L2Basket.cell.nml')
net.cells.append(l2b_cell)
l5b_cell = Cell(id='CELL_HH_simple_L5Basket',
neuroml2_source_file='../CELL_HH_simple_L5Basket.cell.nml')
net.cells.append(l5b_cell)
input_source_poisson100 = InputSource(id='poissonFiringSyn100Hz',
neuroml2_source_file='../inputs.nml')
net.input_sources.append(input_source_poisson100)
input_offset_curr_l2p = InputSource(id='input_offset_curr_l2p',
pynn_input='DCSource',
parameters={
'amplitude': 'offset_curr_l2p',
'start': 0,
'stop': 1e9
})
net.input_sources.append(input_offset_curr_l2p)
l2 = RectangularRegion(id='L2',
x=0,
y=1000,
z=0,
width=1000,
height=10,
depth=1000)
net.regions.append(l2)
l5 = RectangularRegion(id='L5',
x=0,
y=0,
z=0,
width=1000,
height=10,
depth=1000)
net.regions.append(l5)
#https://github.com/OpenSourceBrain/OpenCortex
import opencortex.utils.color as occ
pop_l2p = Population(id='pop_l2p',
size='np2',
component=l2p_cell.id,
properties={'color': occ.L23_PRINCIPAL_CELL},
random_layout=RandomLayout(region=l2.id))
net.populations.append(pop_l2p)
pop_l5p = Population(id='pop_l5p',
size='np5',
component=l5p_cell.id,
properties={'color': occ.L5_PRINCIPAL_CELL},
random_layout=RandomLayout(region=l5.id))
net.populations.append(pop_l5p)
pop_l2b = Population(id='pop_l2b',
size='nb2',
component=l2b_cell.id,
properties={'color': occ.L23_INTERNEURON},
random_layout=RandomLayout(region=l2.id))
net.populations.append(pop_l2b)
pop_l5b = Population(id='pop_l5b',
size='nb5',
component=l5b_cell.id,
properties={'color': occ.L5_INTERNEURON},
random_layout=RandomLayout(region=l5.id))
net.populations.append(pop_l5b)
# L2 -> L2
_add_projection(pop_l2p,
pop_l2b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l2p,
'L2Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l2p,
'L2Pyr_GABAB',
delay=0,
weight=0.001,
probability=0.8,
net=net)
# L2 -> L5
_add_projection(pop_l2p,
pop_l5p,
'L5Pyr_AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2p,
pop_l5b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l5p,
'L5Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
# L5 -> L5
_add_projection(pop_l5p,
pop_l5b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l5b,
pop_l5p,
'L5Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l5b,
pop_l5p,
'L5Pyr_GABAB',
delay=0,
weight=0.001,
probability=0.8,
net=net)
net.inputs.append(
Input(id='stim_%s' % pop_l2p.id,
input_source=input_source_poisson100.id,
population=pop_l2p.id,
percentage=100,
weight='weight_bkg_l2p'))
net.inputs.append(
Input(id='stim_%s' % pop_l5p.id,
input_source=input_source_poisson100.id,
population=pop_l5p.id,
percentage=100,
weight='weight_bkg_l5p'))
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='Sim%s' % net.id,
network=new_file,
duration='500',
seed='1111',
dt='0.025',
recordTraces={'all': recordTraces},
recordSpikes={'all': '*'})
sim.to_json_file()
print(sim.to_json())
return sim, net
def generate(ref="Example6_PyNN", add_inputs=True):
################################################################################
### Build new network
net = Network(id=ref, notes="Another network for PyNN - work in progress...")
net.parameters = {
"N_scaling": 0.005,
"layer_height": 400,
"width": 100,
"depth": 100,
"input_weight": 0.1,
}
cell = Cell(id="CorticalCell", pynn_cell="IF_curr_exp")
cell.parameters = {
"cm": 0.25, # nF
"i_offset": 0.0, # nA
"tau_m": 10.0, # ms
"tau_refrac": 2.0, # ms
"v_reset": -65.0, # mV
"v_rest": -65.0, # mV
"v_thresh": -50.0, # mV
}
net.cells.append(cell)
if add_inputs:
input_cell = Cell(id="InputCell", pynn_cell="SpikeSourcePoisson")
input_cell.parameters = {"start": 0, "duration": 10000000000, "rate": 150}
net.cells.append(input_cell)
e_syn = Synapse(
id="ampa",
pynn_receptor_type="excitatory",
pynn_synapse_type="curr_exp",
parameters={"tau_syn": 0.5},
)
net.synapses.append(e_syn)
i_syn = Synapse(
id="gaba",
pynn_receptor_type="inhibitory",
pynn_synapse_type="curr_exp",
parameters={"tau_syn": 0.5},
)
net.synapses.append(i_syn)
N_full = {
"L23": {"E": 20683, "I": 5834},
"L4": {"E": 21915, "I": 5479},
"L5": {"E": 4850, "I": 1065},
"L6": {"E": 14395, "I": 2948},
}
scale = 0.1
pops = []
input_pops = []
pop_dict = {}
layers = ["L23"]
layers = ["L23", "L4", "L5", "L6"]
for l in layers:
i = 3 - layers.index(l)
r = RectangularRegion(
id=l,
x=0,
y=i * net.parameters["layer_height"],
z=0,
width=net.parameters["width"],
height=net.parameters["layer_height"],
depth=net.parameters["depth"],
)
net.regions.append(r)
for t in ["E", "I"]:
try:
import opencortex.utils.color as occ
if l == "L23":
if t == "E":
color = occ.L23_PRINCIPAL_CELL
if t == "I":
color = occ.L23_INTERNEURON
if l == "L4":
if t == "E":
color = occ.L4_PRINCIPAL_CELL
if t == "I":
color = occ.L4_INTERNEURON
if l == "L5":
if t == "E":
color = occ.L5_PRINCIPAL_CELL
if t == "I":
color = occ.L5_INTERNEURON
if l == "L6":
if t == "E":
color = occ.L6_PRINCIPAL_CELL
if t == "I":
color = occ.L6_INTERNEURON
except:
color = ".8 0 0" if t == "E" else "0 0 1"
pop_id = "%s_%s" % (l, t)
pops.append(pop_id)
ref = "l%s%s" % (l[1:], t.lower())
exec(
ref
+ " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % ref)
exec("net.populations.append(%s)" % ref)
exec("pop_dict['%s'] = %s" % (pop_id, ref))
if add_inputs:
color = ".8 .8 .8"
input_id = "%s_%s_input" % (l, t)
input_pops.append(input_id)
input_ref = "l%s%s_i" % (l[1:], t.lower())
exec(
input_ref
+ " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref)
exec("net.populations.append(%s)" % input_ref)
# l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':})
# l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id)
# l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id)
# net.populations.append(l23e)
# net.populations.append(l23ei)
# net.populations.append(l23i)
# net.populations.append(l23ii)
conn_probs = [
[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0.0, 0.0076, 0.0],
[0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0.0, 0.0042, 0.0],
[0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.0],
[0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0.0, 0.1057, 0.0],
[0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.0],
[0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.0],
[0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
[0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443],
]
if add_inputs:
for p in pops:
proj = Projection(
id="proj_input_%s" % p,
presynaptic="%s_input" % p,
postsynaptic=p,
synapse=e_syn.id,
delay=2,
weight="input_weight",
)
proj.one_to_one_connector = OneToOneConnector()
net.projections.append(proj)
for pre_i in range(len(pops)):
for post_i in range(len(pops)):
pre = pops[pre_i]
post = pops[post_i]
prob = conn_probs[post_i][pre_i] ####### TODO: check!!!!
weight = 1
syn = e_syn
if prob > 0:
if "I" in pre:
weight = -1
syn = i_syn
proj = Projection(
id="proj_%s_%s" % (pre, post),
presynaptic=pre,
postsynaptic=post,
synapse=syn.id,
delay=1,
weight=weight,
)
proj.random_connectivity = RandomConnectivity(probability=prob)
net.projections.append(proj)
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Build Simulation object & save as JSON
record_traces = {}
record_spikes = {}
from neuromllite.utils import evaluate
for p in pops:
forecast_size = evaluate(pop_dict[p].size, net.parameters)
record_traces[p] = list(range(min(2, forecast_size)))
record_spikes[p] = "*"
for ip in input_pops:
record_spikes[ip] = "*"
sim = Simulation(
id="Sim%s" % net.id,
network=new_file,
duration="100",
dt="0.025",
seed=1234,
record_traces=record_traces,
record_spikes=record_spikes,
)
sim.to_json_file()
return sim, net
