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)
