def parameter_scan(p=None):
if p is None:
p = default_parameters()
#p.K2 = ParameterRange([0.7,0.0])
#p.dt = ParameterRange([1.,6.77,10.,1000.]) # ms
#p.td = ParameterRange([0.])#,6.,20.])
#p.t1 = ParameterRange([0.]) # ms
#p.t2 = ParameterRange([0.]) # ms
#g = 0.8
#p.c1 = ParameterRange([0.14*g])
#p.c2 = ParameterRange([0.12*g])
p.sigma_c = ParameterRange([0.1,0.4,0.8])
create_figure()
for experiment in p.iter_inner():
name = make_name(experiment,p.range_keys())
print name
plot_receptive_field(p=experiment,label=name)
# creating a results array, with the dimensions of the ParameterSpace
corrcoef_results = numpy.empty(dims)
# scanning the ParameterSpace
for experiment in p.iter_inner():
# calculation of the index in the space
index = p.parameter_space_index(experiment)
# perfomring the experiment
cc,time_axis_cc, corrcoef = calc_cc(experiment)
corrcoef_results[index] = corrcoef
# plotting the cc's
subplot_index = (dims[1]*index[0])+index[1]
pylab.subplot(dims[0],dims[1],subplot_index+1)
pylab.plot(time_axis_cc,cc)
pylab.title(make_name(experiment,p.range_keys()))
pylab.xlim(-30,30.)
pylab.ylim(0,10.)
# plot the results
pylab.matshow(corrcoef_results)
pylab.xticks(numpy.arange(0.5,dims[1]+0.5,1.0),[str(i) for i in p.jitter._values])
pylab.yticks(numpy.arange(0.5,dims[0]+0.5,1.0),[str(i) for i in p.c._values])
pylab.xlim(0,dims[1])
pylab.ylim(dims[0],0)
pylab.xlabel('jitter (ms)')
pylab.ylabel('correlation')
ax = pylab.colorbar()
ax.set_label('correlation')
pylab.draw()
# creating a results array, with the dimensions of the ParameterSpace
corrcoef_results = numpy.empty(dims)
# scanning the ParameterSpace
for experiment in p.iter_inner():
# calculation of the index in the space
index = p.parameter_space_index(experiment)
# perfomring the experiment
cc, time_axis_cc, corrcoef = calc_cc(experiment)
corrcoef_results[index] = corrcoef
# plotting the cc's
subplot_index = (dims[1] * index[0]) + index[1]
pylab.subplot(dims[0], dims[1], subplot_index + 1)
pylab.plot(time_axis_cc, cc)
pylab.title(make_name(experiment, p.range_keys()))
pylab.xlim(-30, 30.)
pylab.ylim(0, 10.)
# plot the results
pylab.matshow(corrcoef_results)
pylab.xticks(numpy.arange(0.5, dims[1] + 0.5, 1.0),
[str(i) for i in p.jitter._values])
pylab.yticks(numpy.arange(0.5, dims[0] + 0.5, 1.0),
[str(i) for i in p.c._values])
pylab.xlim(0, dims[1])
pylab.ylim(dims[0], 0)
pylab.xlabel('jitter (ms)')
pylab.ylabel('correlation')
ax = pylab.colorbar()
ax.set_label('correlation')
# 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)
index = p.parameter_space_index(experiment)
results[index] = 0