p.gL = 25.
p.tref = 2.
# Parameters for running
p.timestep = 0.1
p.min_delay = 0.1
p.max_delay = 5.1
p.runtime = 500000.
# Parameters for number and connections
p.r0 = 1400. # excitatory input rate
p.ri = 1647. # inhibitory input rate
p.je = 0.015 # excitatory synaptic weight
p.ji = -0.015 # inhibitory synaptic weight
p.cb = ParameterRange(numpy.arange(
1., 1.001,
0.05)) # between-cell correlation of background input (NSD input)
p.N = 1000 # number of daughter cells for MIP
p.r = ParameterRange(numpy.arange(100., 101., 400.)) # MIP rate
p.c = ParameterRange(numpy.array([0.05])) # with-pool correlation in MIP
p.q = ParameterRange(numpy.arange(
0.2, 0.2501, 0.05)) # between-cell correlation of MIP input (SD input)
p.edge = ParameterRange(numpy.array([0.])) # range of temporal jitter of MIP
dims, labels = p.parameter_space_dimension_labels()
results = numpy.empty(dims)
for experiment in p.iter_inner():
name = make_name(experiment, p.range_keys())
print name
model = st.Striatum()
model.run(sim, experiment, name)
p.E_ex = 0.
p.E_in = -70.
p.ie = 0.
p.cm = 500.
p.gL = 25.
p.tref = 2.
# Parameters for running
p.timestep = 0.1
p.min_delay = 0.1
p.max_delay = 5.1
p.runtime = 100000.
# Parameters for number and connections
p.r0 = 2000. # excitatory input rate
p.ri = 1647. # inhibitory input rate
p.je = 0.015 # excitatory synaptic weight
p.ji = -0.015 # inhibitory synaptic weight
p.cb = ParameterRange(numpy.arange(0., 1.001,
0.05)) # between-cell correlation
dims, labels = p.parameter_space_dimension_labels()
results = numpy.empty(dims)
for experiment in p.iter_inner():
name = make_name(experiment, p.range_keys())
print name
model = st.Striatum()
model.run(sim, experiment, name)
index = p.parameter_space_index(experiment)
results[index] = 0