# instantiate Network:
network = Network(**networkParameters)
# create E and I populations:
for name, size in zip(population_names, population_sizes):
network.create_population(name=name, POP_SIZE=size,
**populationParameters)
# create excitatory background synaptic activity for each cell
# with Poisson statistics
for cell in network.populations[name].cells:
idx = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
for i in idx:
syn = Synapse(cell=cell, idx=i, syntype='Exp2Syn',
weight=0.002,
**dict(tau1=0.2, tau2=1.8, e=0.))
syn.set_spike_times_w_netstim(interval=100.,
seed=np.random.rand() * 2**32 - 1
)
# create connectivity matrices and connect populations:
for i, pre in enumerate(population_names):
for j, post in enumerate(population_names):
# boolean connectivity matrix between pre- and post-synaptic neurons
# in each population (postsynaptic on this RANK)
connectivity = network.get_connectivity_rand(
pre=pre, post=post,
connprob=connectionProbability[i][j]
)
# create excitatpry background synaptic activity for each cell
# with Poisson statistics
k = 0
for cell in network.populations[name].cells:
#rotation = {'x' : np.random.uniform(0,2*np.pi), 'y' : np.random.uniform(0,2*np.pi)}
#cell.set_rotation(**rotation)
#i = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
noise = 0.1
t1 = 0.2
t2 = 5.0
if 20 > k >= 0:
syn = Synapse(cell=cell,
idx=0,
syntype='Exp2Syn',
weight=0.006,
**dict(tau1=t1, tau2=t2, e=0.))
syn.set_spike_times_w_netstim(interval=10.,
noise=noise,
start=200.,
number=35)
elif 40 > k >= 20:
syn = Synapse(cell=cell,
idx=0,
syntype='Exp2Syn',
weight=0.006,
**dict(tau1=t1, tau2=t2, e=0.))
syn.set_spike_times_w_netstim(interval=10.,
noise=noise,
start=220.,
if RANK == 0:
print(ii, name, size)
network.create_population(name=name,
POP_SIZE=size,
**populationParameters[ii])
#initial train and all spikes
if name == 'E': #Receiving cell is excitatory
for j, cell in enumerate(network.populations[name].cells):
if j % 4 == 0:
weighttrain = np.random.normal(0.05, 0.02)
idx = cell.get_rand_idx_area_norm(section='dend', nidx=1)
for i in idx:
syn = Synapse(cell=cell,
idx=i,
syntype='Exp2Syn',
weight=weighttrain,
**dict(synapseParameters[0][0]))
syn.set_spike_times(np.array([distr_t[j]]))
for i in range(len(syns[0])): #E:E og ei
if syns[0][i][0] == j: #ii første?
pre_gid = syns[0][i][1]
ind = SPIKES['gids'][0].index(pre_gid) #i her?
tim = SPIKES['times'][0][ind]
if tim.size > 0:
# print("EE", tim)
x = syns[0][i][2]
y = syns[0][i][3]
z = syns[0][i][4]
idx = cell.get_closest_idx(x, y, z)
network = Network(**networkParameters)
# create populations
for name, size in zip(population_names, population_sizes):
network.create_population(name=name,
POP_SIZE=size,
**populationParameters)
# create some background synaptic activity onto the cells with Poisson
# activation statistics
for cell in network.populations[name].cells:
idx = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
for i in idx:
syn = Synapse(cell=cell,
idx=i,
syntype='Exp2Syn',
weight=0.002,
**dict(tau1=0.2, tau2=1.8, e=0.))
syn.set_spike_times_w_netstim(interval=1000. / 5.)
# create connectivity and connect populations
for i, pre in enumerate(population_names):
for j, post in enumerate(population_names):
# boolean connectivity matrix between pre- and post-synaptic neurons
# in each population (postsynaptic on this RANK)
connectivity = network.get_connectivity_rand(
pre=pre, post=post, connprob=connectionProbability)
# connect network
(conncount, syncount) = network.connect(
pre=pre,
for ii, (name, size) in enumerate(zip(population_names, population_sizes)):
if RANK == 0:
print(ii, name, size)
network.create_population(name=name,
POP_SIZE=size,
**populationParameters[ii])
# initial spike train
if name == 'E':
for j, cell in enumerate(network.populations[name].cells):
if j % 4 == 0:
idx = cell.get_rand_idx_area_norm(section='dend', nidx=1)
for i in idx: #if more than one synapse
syn = Synapse(cell=cell,
idx=i,
syntype='Exp2Syn',
weight=weighttrain,
**dict(synapseParameters[0][0]))
syn.set_spike_times(np.array([distr_t[j]]))
# create connectivity matrices and connect populations:
for i, pre in enumerate(population_names):
for j, post in enumerate(population_names):
# boolean connectivity matrix between pre- and post-synaptic neurons
# in each population (postsynaptic on this RANK)
connectivity = network.get_connectivity_rand(
pre=pre, post=post, connprob=connectionProbability[i][j])
print(np.shape(connectivity))
# connect network:
(conncount, syncount) = network.connect(
# instantiate Network:
network = Network(**networkParameters)
# create E and I populations:
for name, size in zip(population_names, population_sizes):
network.create_population(name=name, POP_SIZE=size,
**populationParameters)
# create excitatpry background synaptic activity for each cell
# with Poisson statistics
for cell in network.populations[name].cells:
idx = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
for i in idx:
syn = Synapse(cell=cell, idx=i, syntype='Exp2Syn',
weight=0.002,
**dict(tau1=0.2, tau2=1.8, e=0.))
syn.set_spike_times_w_netstim(interval=200.)
# create connectivity matrices and connect populations:
for i, pre in enumerate(population_names):
for j, post in enumerate(population_names):
# boolean connectivity matrix between pre- and post-synaptic neurons
# in each population (postsynaptic on this RANK)
connectivity = network.get_connectivity_rand(
pre=pre, post=post,
connprob=connectionProbability[i][j]
)
# connect network: