tp = np.loadtxt(params['tuning_prop_means_fn']) # spatial distribution of inhibitory cells hexgrid_pos = utils.set_hexgrid_positions(params, params['N_RF_X_INH'], params['N_RF_Y_INH']) output_fn = params['inh_cell_pos_fn'] print 'debug hexgrid pos shape', hexgrid_pos.shape print 'printing inh cell positions to:', output_fn np.savetxt(output_fn, hexgrid_pos) # for each exc cell calculate the predicted position of the dot from the cell's tuning prop predicted_positions = utils.get_predicted_stim_pos(tp) # x_predicted = x + v [a.u.] print "Predicted positions: ", params['predicted_positions_fn'] np.savetxt(params['predicted_positions_fn'], predicted_positions) # calculate the ring of excitatory source cells for each target inhibitory cell src_pos = predicted_positions tgt_pos = hexgrid_pos import CreateConnections as CC n_ei_per_cell = int(round(params['p_ei'] * params['n_exc'])) print 'Each inh cell receives input from %d exc cells (p_ei = %.3f)' % (n_ei_per_cell, params['p_ei']) output_indices, output_distances = CC.get_exc_inh_connections(src_pos, tgt_pos, tp, n=n_ei_per_cell) output_fn1 = params['exc_inh_adjacency_list_fn'] output_fn2 = params['exc_inh_distances_fn'] print 'saving exc-inh connections', output_fn1 print 'saving exc-inh distances ', output_fn2 np.savetxt(output_fn1, output_indices) np.savetxt(output_fn2, output_distances) # TODO: convert #self.params['exc_inh_weights_fn'] = '%sexc_to_inh_indices.dat' % (self.params['connections_folder']) # same format as exc_inh_distances_fn, containing the exc - inh weights
