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)