def get_intra_network_conductances(self, conn_list_fn=None):
# calculate the (mean, std) conductances between different groups of neurons
# good (g) -> good
# g -> rest (r)
# r -> g
# r -> r
# g -> inh
# r -> inh
# all exc -> inh
# inh -> all exc
# inh -> r
# inh -> g
if conn_list_fn == None:
conn_list_fn = self.params['merged_conn_list_ee']
print 'utils.get_conn_dict from file:', conn_list_fn
self.conn_dict = utils.get_conn_dict(self.params, conn_list_fn)
spike_fn = self.params['exc_spiketimes_fn_merged'] + '.ras'
def __init__(self, params=None):
if params == None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
# P = network_params.load_params() # params stores cell numbers, etc as a dictionary
self.params = network_params.params
else:
self.params = params
self.n_exc = self.params['n_exc']
self.output = []
self.g_in_histograms = []
self.output_fig = self.params['conductances_fig_fn_base']
self.n_good = self.params['n_exc'] * .05 # fraction of 'good' (well-tuned) cells
print 'Number of \'good\' (well-tuned) cells:', self.n_good
self.no_spikes = False
self.load_nspikes()
self.conn_dict = {}
for conn_type in self.params['conn_types']:
print 'Calling utils.get_conn_dict(..., %s)' % conn_fn
conn_fn = self.params['conn_list_%s_fn' % conn_type]
self.conn_dict[conn_type] = utils.get_conn_dict(self.params, conn_fn)
fig_width_pt = 800.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = {#'backend': 'png',
'axes.labelsize': 12,
# 'text.fontsize': 14,
# 'legend.fontsize': 10,
# 'xtick.labelsize': 8,
# 'ytick.labelsize': 8,
# 'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
pylab.subplots_adjust(bottom=0.30)