SIZE = COMM.Get_size()
RANK = COMM.Get_rank()
# load some neuron-interface files needed for the EPFL cell types
neuron.h.load_file("stdrun.hoc")
neuron.h.load_file("import3d.hoc")
# test mode (1 cell per pop, all-to-all connectivity)
TESTING = False
# Creating a NeuroTools.parameters.ParameterSet object for the main parameters
PSET = ParameterSet({})
# output file destination
if TESTING:
PSET.OUTPUTPATH = 'example_parallel_network_output_testing'
else:
PSET.OUTPUTPATH = 'example_parallel_network_output'
# input file paths
# PATHs to current cell-specific files and NMODL files
PSET.CWD = os.getcwd()
PSET.CELLPATH = 'hoc_combos_syn.1_0_10.allzips'
PSET.NMODL = 'hoc_combos_syn.1_0_10.allmods'
########################################################
# Simulation control
########################################################
PSET.dt = 2**-4 # simulation time step size (ms)
PSET.tstop = 1500. # simulation duration (ms)
PSET.v_init = -77. # membrane voltage(s) at t = 0 for all cells
# load some neuron-interface files needed for the EPFL cell types
neuron.h.load_file("stdrun.hoc")
neuron.h.load_file("import3d.hoc")
# test mode (1 cell per pop, all-to-all connectivity)
TESTING = False
# Creating a NeuroTools.parameters.ParameterSet object for the main parameters
PSET = ParameterSet({})
# output file destination
if TESTING:
PSET.OUTPUTPATH = 'example_parallel_network_output_testing'
else:
PSET.OUTPUTPATH = 'example_parallel_network_output'
# input file paths
# PATHs to current cell-specific files and NMODL files
PSET.CWD = os.getcwd()
PSET.CELLPATH = 'hoc_combos_syn.1_0_10.allzips'
PSET.NMODL = 'hoc_combos_syn.1_0_10.allmods'
########################################################
# Simulation control
########################################################
PSET.dt = 2**-4 # simulation time step size (ms)
PSET.tstop = 1500. # simulation duration (ms)
# scaling 1 with the population scaling variable, and recompute
# the corresponding connection probability)
PSET.connParams['connprob'][i][j] = \
1. - (1. - 1. / (N_pre * N_post)
)**(PSET.POPSCALING * np.log(
1. - PSET.connParams['connprob'][i][j]
) / np.log(
1. - PSET.POPSCALING**2 / (N_pre * N_post)))
if RANK == 0:
print(string + ', C1={}, K1={}'.format(
PSET.connParams['connprob'][i][j],
np.log(1. - PSET.connParams['connprob'][i][j]) /
np.log(1. - 1. / (N_pre * N_post))))
# file output destination
PSET.OUTPUTPATH = os.path.join(OUTPUT, ps_id)
# avoid same sequence of random numbers from numpy and neuron on each RANK,
# e.g., in order to draw unique cell locations and random synapse
# activation times
np.random.seed(PSET.GLOBALSEED + RANK)
# set up file destination
if not os.path.isdir(PSET.OUTPUTPATH):
if RANK == 0:
os.mkdir(PSET.OUTPUTPATH)
COMM.Barrier()
if RANK == 0:
parameters_time = time() - tic
# open file object for writing