def generate_synapse_list(self, prevertex, postvertex, delay_scale, synapse_type): id_lists = list() weight_lists = list() delay_lists = list() type_lists = list() for _ in range(0, prevertex.atoms): id_lists.append(list()) weight_lists.append(list()) delay_lists.append(list()) type_lists.append(list()) pre_synaptic_neurons = random.sample(range(0, prevertex.atoms), self.n_pre) for pre_atom in pre_synaptic_neurons: present = numpy.ones(postvertex.atoms, dtype=numpy.uint32) n_present = postvertex.atoms id_lists[pre_atom] = numpy.where(present)[0] weight_lists[pre_atom] = generateParameterArray(self.weights, n_present, present) delay_lists[pre_atom] = (generateParameterArray(self.delays, n_present, present) * delay_scale) type_lists[pre_atom] = synapse_type connection_list = [SynapseRowInfo(id_lists[i], weight_lists[i], delay_lists[i], type_lists[i]) for i in range(0, prevertex.atoms)] return SynapticList(connection_list)
def generate_synapse_list(self, prevertex, postvertex, delay_scale, synapse_type): id_lists = list() weight_lists = list() delay_lists = list() type_lists = list() for _ in range(0, prevertex.atoms): id_lists.append(list()) weight_lists.append(list()) delay_lists.append(list()) type_lists.append(list()) pre_synaptic_neurons = random.sample(range(0, prevertex.atoms), self.n_pre) for pre_atom in pre_synaptic_neurons: present = numpy.ones(postvertex.atoms, dtype=numpy.uint32) n_present = postvertex.atoms id_lists[pre_atom] = numpy.where(present)[0] weight_lists[pre_atom] = generateParameterArray( self.weights, n_present, present) delay_lists[pre_atom] = ( generateParameterArray(self.delays, n_present, present) * delay_scale) type_lists[pre_atom] = synapse_type connection_list = [ SynapseRowInfo(id_lists[i], weight_lists[i], delay_lists[i], type_lists[i]) for i in range(0, prevertex.atoms) ] return SynapticList(connection_list)
def generate_synapse_list(self, prevertex, postvertex, delay_scale, synapse_type): rows = list() for _ in range(0, prevertex.atoms): present = numpy.random.rand(postvertex.atoms) <= self.p_connect n_present = numpy.sum(present) ids = numpy.where(present)[0] delays = (generateParameterArray(self.delays, n_present, present) * delay_scale) weights = generateParameterArray(self.weights, n_present, present) synapse_types = (numpy.ones(len(ids), dtype='uint32') * synapse_type) rows.append(SynapseRowInfo(ids, weights, delays, synapse_types)) return SynapticList(rows)
def generate_synapse_list(self, prevertex, postvertex, delay_scale, synapse_type): connection_list = list() for _ in range(0, prevertex.atoms): present = numpy.ones(postvertex.atoms, dtype=numpy.uint32) n_present = postvertex.atoms ids = numpy.where(present)[0] delays = (generateParameterArray(self.delays, n_present, present) * delay_scale) weights = generateParameterArray(self.weights, n_present, present) synapse_types = (numpy.ones(len(ids), dtype='uint32') * synapse_type) connection_list.append( SynapseRowInfo(ids, weights, delays, synapse_types)) return SynapticList(connection_list)
def generate_synapse_list(self, prevertex, postvertex, delay_scale, synapse_type): connection_list = list() for _ in range(0, prevertex.atoms): present = numpy.ones(postvertex.atoms, dtype=numpy.uint32) n_present = postvertex.atoms ids = numpy.where(present)[0] delays = (generateParameterArray(self.delays, n_present, present) * delay_scale) weights = generateParameterArray(self.weights, n_present, present) synapse_types = (numpy.ones(len(ids), dtype='uint32') * synapse_type) connection_list.append(SynapseRowInfo(ids, weights, delays, synapse_types)) return SynapticList(connection_list)