def connect_anisotropic(self, conn_type):
"""
conn_type = ['ee', 'ei', 'ie', 'ii']
"""
if self.pc_id == 0:
print 'Connect anisotropic %s - %s' % (conn_type[0].capitalize(), conn_type[1].capitalize())
(n_src, n_tgt, src_pop, tgt_pop, tp_src, tp_tgt, tgt_cells, syn_type) = self.resolve_src_tgt(conn_type)
if self.debug_connectivity:
conn_list_fn = self.params['conn_list_%s_fn_base' % conn_type] + '%d.dat' % (self.pc_id)
n_src_cells_per_neuron = int(round(self.params['p_%s' % conn_type] * n_src))
(delay_min, delay_max) = self.params['delay_range']
local_connlist = np.zeros((n_src_cells_per_neuron * len(tgt_cells), 4))
for i_, tgt in enumerate(tgt_cells):
if self.params['conn_conf'] == 'direction-based':
p, latency = CC.get_p_conn_direction_based(tp_src, tp_tgt[tgt, :], self.params['w_sigma_x'], self.params['w_sigma_v'], self.params['connectivity_radius'])
elif self.params['conn_conf'] == 'motion-based':
p, latency = CC.get_p_conn_motion_based(tp_src, tp_tgt[tgt, :], self.params['w_sigma_x'], self.params['w_sigma_v'], self.params['connectivity_radius'])
elif self.params['conn_conf'] == 'orientation-direction':
p, latency = CC.get_p_conn_direction_and_orientation_based(tp_src, tp_tgt[tgt, :], self.params['w_sigma_x'], self.params['w_sigma_v'], self.params['w_sigma_theta'], self.params['connectivity_radius'])
else:
print '\n\nERROR! Wrong connection configutation conn_conf parameter provided\nShould be direction-based, motion-based or orientation-direction\n'
exit(1)
if conn_type[0] == conn_type[1]:
p[tgt], latency[tgt] = 0., 0.
# random delays? --> np.permutate(latency) or latency[sources] * self.params['delay_scale'] * np.rand
sorted_indices = np.argsort(p)
if conn_type[0] == 'e':
sources = sorted_indices[-n_src_cells_per_neuron:]
else: # source = inhibitory
if conn_type[0] == conn_type[1]:
sources = sorted_indices[1:n_src_cells_per_neuron+1] # shift indices to avoid self-connection, because p_ii = .0
else:
sources = sorted_indices[:n_src_cells_per_neuron]
eta = 1e-12
w = (self.params['w_tgt_in_per_cell_%s' % conn_type] / (p[sources].sum() + eta)) * p[sources]
w_ = np.minimum(np.maximum(w, self.params['w_thresh_min']), self.params['w_thresh_max'])
delays = np.minimum(np.maximum(latency[sources] * self.params['delay_scale'], delay_min), delay_max) # map the delay into the valid range
conn_list = np.array((sources, tgt * np.ones(n_src_cells_per_neuron), w_, delays))
local_connlist[i_ * n_src_cells_per_neuron : (i_ + 1) * n_src_cells_per_neuron, :] = conn_list.transpose()
connector = FromListConnector(conn_list.transpose())
if self.params['with_short_term_depression']:
prj = Projection(src_pop, tgt_pop, connector, target=syn_type, synapse_dynamics=self.short_term_depression)
else:
prj = Projection(src_pop, tgt_pop, connector, target=syn_type)
self.projections[conn_type].append(prj)
if self.debug_connectivity:
if self.pc_id == 0:
print 'DEBUG writing to file:', conn_list_fn
np.savetxt(conn_list_fn, local_connlist, fmt='%d\t%d\t%.4e\t%.4e')
