syntype=PSET.connParamsExtrinsic['syntype'],
weight=weightfun(**weightargs),
**synparams)
syn.set_spike_times_w_netstim(interval=1000. / f)
# connect pre and post-synaptic populations with some connectivity and
# weight of connections and other connection parameters:
total_conncount = 0
total_syncount = 0
for i, pre in enumerate(PSET.populationParameters['me_type']):
for j, post in enumerate(PSET.populationParameters['me_type']):
# 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=PSET.connParams['connprob'][i][j])
# connect network
(conncount, syncount) = network.connect(
pre=pre, post=post,
connectivity=connectivity,
syntype=PSET.connParams['syntypes'][i][j],
synparams=PSET.connParams['synparams'][i][j],
weightfun=PSET.connParams['weightfuns'][i][j],
weightargs=PSET.connParams['weightargs'][i][j],
delayfun=PSET.connParams['delayfuns'][i][j],
delayargs=PSET.connParams['delayargs'][i][j],
multapsefun=PSET.connParams['multapsefuns'][i][j],
multapseargs=PSET.connParams['multapseargs'][i][j],
syn_pos_args=PSET.connParams['syn_pos_args'][i][j],
save_connections=PSET.save_connections,
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]
)
# connect network:
(conncount, syncount) = network.connect(
pre=pre, post=post,
connectivity=connectivity,
syntype=synapseModel,
synparams=synapseParameters[i][j],
weightfun=np.random.normal,
weightargs=weightArguments[i][j],
minweight=minweight,
delayfun=delayFunction,
delayargs=delayArguments[i][j],
mindelay=mindelay,
multapsefun=multapseFunction,
syn = LFPy.Synapse(cell=cell,
idx=i,
syntype=PSET.connParamsExtrinsic['syntype'],
weight=weightfun(**weightargs),
**synparams)
syn.set_spike_times_w_netstim(interval=1000. / f)
# connect pre and post-synaptic populations with some connectivity and
# weight of connections and other connection parameters:
total_conncount = 0
total_syncount = 0
for i, pre in enumerate(PSET.populationParameters['me_type']):
for j, post in enumerate(PSET.populationParameters['me_type']):
# 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=PSET.connParams['connprob'][i][j])
# connect network
(conncount, syncount) = network.connect(
pre=pre,
post=post,
connectivity=connectivity,
syntype=PSET.connParams['syntypes'][i][j],
synparams=PSET.connParams['synparams'][i][j],
weightfun=PSET.connParams['weightfuns'][i][j],
weightargs=PSET.connParams['weightargs'][i][j],
delayfun=PSET.connParams['delayfuns'][i][j],
delayargs=PSET.connParams['delayargs'][i][j],
multapsefun=PSET.connParams['multapsefuns'][i][j],
multapseargs=PSET.connParams['multapseargs'][i][j],
syn_pos_args=PSET.connParams['syn_pos_args'][i][j],
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:
(conncount, syncount) = network.connect(
pre=pre, post=post,
connectivity=connectivity,
syntype=synapseModel,
synparams=synapseParameters[i][j],
weightfun=np.random.normal,
weightargs=weightArguments[i][j],
minweight=minweight,
delayfun=delayFunction,
delayargs=delayArguments[i][j],
mindelay=mindelay,
multapsefun=multapseFunction,