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Example6.py
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Example6.py
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from neuromllite import Network, Cell, Population, Simulation, Synapse
from neuromllite import RectangularRegion, RandomLayout
from neuromllite import Projection, RandomConnectivity, OneToOneConnector
import sys
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
if __name__ == "__main__":
if "-noinputs" in sys.argv:
sim, net = generate("Example6_PyNN_noinputs", False)
else:
sim, net = generate("Example6_PyNN", True)
################################################################################
### Run in some simulators
from neuromllite.NetworkGenerator import check_to_generate_or_run
check_to_generate_or_run(sys.argv, sim)