PSET.populationParameters['me_type'],
pathways_physiology, mtype_map, synapses_tsv)
del synapses_tsv # not needed anymore.
###########################################################################
# Set up main connection parameters used by Network class instance methods
############################################################################
# Main connection parameters between pre and post-synaptic populations organized as
# dictionary of parameter lists between pre and postsynaptic populations:
if PSET.fully_connected:
connprob = [[1]*PSET.populationParameters.size]*PSET.populationParameters.size # fully connected network (no selfconnections)
else:
connprob = get_params(PSET.populationParameters['m_type'], pathways_anatomy,
'connection_probability', 0.01*PSET.CONNPROBSCALING) #unit conversion % -> fraction, increase weight ewith diluted network
PSET.connParams = dict(
# connection probabilities between populations
connprob = connprob,
# synapse mechanisms
syntypes = [[neuron.h.ProbAMPANMDA_EMS
if syn_param_stats['{}:{}'.format(pre, post)]['synapse_type'] >= 100 else
neuron.h.ProbGABAAB_EMS for post in PSET.populationParameters['m_type']] for pre in PSET.populationParameters['m_type']],
# synapse time constants and reversal potentials.
# Use the mean/global EPFL synapse model parameters
# (for now) as some connections appear to be missing in pathway files.
synparams = [[ dict(
del synapses_tsv # not needed anymore.
###########################################################################
# Set up main connection parameters used by Network class instance methods
############################################################################
# Main connection parameters between pre and post-synaptic populations organized as
# dictionary of parameter lists between pre and postsynaptic populations:
if PSET.fully_connected:
connprob = [
[1] * PSET.populationParameters.size
] * PSET.populationParameters.size # fully connected network (no selfconnections)
else:
connprob = get_params(
PSET.populationParameters['m_type'], pathways_anatomy,
'connection_probability', 0.01 * PSET.CONNPROBSCALING
) #unit conversion % -> fraction, increase weight ewith diluted network
PSET.connParams = dict(
# connection probabilities between populations
connprob=connprob,
# synapse mechanisms
syntypes=[[
neuron.h.ProbAMPANMDA_EMS
if syn_param_stats['{}:{}'.format(pre, post)]['synapse_type'] >= 100
else neuron.h.ProbGABAAB_EMS
for post in PSET.populationParameters['m_type']
] for pre in PSET.populationParameters['m_type']],
# synapse time constants and reversal potentials.