def _connect(self):
cl = []
v = 0
for i in numpy.nonzero(self.target.pop._mask_local)[0]:
samples = self.target.get_neuron_annotation(i,self.parameters.annotation_reference_name)
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
if self.parameters.num_samples == 0:
co = Counter(sample_from_bin_distribution(weights, int(samples)))
else:
assert self.parameters.num_samples > 2*int(samples), ("%s: %d %d" % (self.name,self.parameters.num_samples,2*int(samples)))
co = Counter(sample_from_bin_distribution(weights, int(self.parameters.num_samples - 2*int(samples))))
v = v + numpy.sum(co.values())
k = co.keys()
a = numpy.array([k,numpy.zeros(len(k))+i,self.weight_scaler*numpy.multiply(self.parameters.base_weight.next(len(k)),co.values()),numpy.array(delays)[k]])
cl.append(a)
cl = numpy.hstack(cl).T
method = self.sim.FromListConnector(cl)
logger.warning("%s(%s): %g connections were created, %g per target neuron [%g]" % (self.name,self.__class__.__name__,len(cl),len(cl)/len(numpy.nonzero(self.target.pop._mask_local)[0]),v/len(numpy.nonzero(self.target.pop._mask_local)[0])))
if len(cl) > 0:
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
else:
logger.warning("%s(%s): empty projection - pyNN projection not created." % (self.name,self.__class__.__name__))
def _connect(self):
cl = []
v = 0
for i in numpy.nonzero(self.target.pop._mask_local)[0]:
samples = self.target.get_neuron_annotation(i,self.parameters.annotation_reference_name)
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
if self.parameters.num_samples == 0:
co = Counter(sample_from_bin_distribution(weights, int(samples)))
else:
assert self.parameters.num_samples > 2*int(samples), ("%s: %d %d" % (self.name,self.parameters.num_samples,2*int(samples)))
co = Counter(sample_from_bin_distribution(weights, int(self.parameters.num_samples - 2*int(samples))))
v = v + numpy.sum(co.values())
k = co.keys()
a = numpy.array([k,numpy.zeros(len(k))+i,self.weight_scaler*numpy.multiply(self.parameters.base_weight.next(len(k)),co.values()),numpy.array(delays)[k]])
cl.append(a)
cl = numpy.hstack(cl).T
method = self.sim.FromListConnector(cl)
logger.warning("%s(%s): %g connections were created, %g per target neuron [%g]" % (self.name,self.__class__.__name__,len(cl),len(cl)/len(numpy.nonzero(self.target.pop._mask_local)[0]),v/len(numpy.nonzero(self.target.pop._mask_local)[0])))
if len(cl) > 0:
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
else:
logger.warning("%s(%s): empty projection - pyNN projection not created." % (self.name,self.__class__.__name__))
def worker(ref,idxs):
for i in idxs:
samples = self.target.get_neuron_annotation(i,self.parameters.annotation_reference_name)
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
if self.parameters.num_samples == 0:
co = Counter(sample_from_bin_distribution(weights, int(samples)))
else:
assert self.parameters.num_samples > 2*int(samples), ("%s: %d %d" % (self.name,self.parameters.num_samples,2*int(samples)))
a = sample_from_bin_distribution(weights, int(self.parameters.num_samples - 2*int(samples)))
co = Counter(a)
v = v + numpy.sum(co.values())
cl.extend([(int(k),int(i),self.weight_scaler*self.parameters.base_weight.next()*co[k],delays[k]) for k in co.keys()])
return cl
def worker(ref,idxs):
for i in idxs:
samples = self.target.get_neuron_annotation(i,self.parameters.annotation_reference_name)
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
if self.parameters.num_samples == 0:
co = Counter(sample_from_bin_distribution(weights, int(samples)))
else:
assert self.parameters.num_samples > 2*int(samples), ("%s: %d %d" % (self.name,self.parameters.num_samples,2*int(samples)))
a = sample_from_bin_distribution(weights, int(self.parameters.num_samples - 2*int(samples)))
co = Counter(a)
v = v + numpy.sum(co.values())
cl.extend([(int(k),int(i),self.weight_scaler*self.parameters.base_weight.next()[0]*co[k],delays[k]) for k in co.keys()])
return cl
def _connect(self):
# This is due to native synapses models (which we currently use as the short term synaptic plasticity model)
# do not apply the 1000 factor scaler as the pyNN synaptic models
wf = self.parameters.weight_factor * self.weight_scaler
seeds = mozaik.get_seeds(self.target.pop.size)
weights = self.connection_matrix
delays = self.delay_matrix
cl = []
for i in range(0, self.target.pop.size):
co = Counter(
sample_from_bin_distribution(weights[:, i].flatten(),
int(self.parameters.num_samples),
seeds[i]))
cl.extend([(int(k), int(i),
wf * co[k] / self.parameters.num_samples, delays[k][i])
for k in co.keys()])
method = self.sim.FromListConnector(cl)
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
def build_connector(indices, seeds, queue_cl, queue_v):
cli = []
vi = 0
for i in range(len(indices)):
weights = self._obtain_weights(indices[i], seeds[i])
delays = self._obtain_delays(indices[i], seeds[i])
co = Counter(
sample_from_bin_distribution(
weights, int(self.parameters.num_samples.next()),
seeds[i]))
vi = vi + numpy.sum(list(co.values()))
k = list(co.keys())
a = numpy.array([
k,
numpy.zeros(len(k)) + indices[i],
self.weight_scaler * numpy.multiply(
self.parameters.base_weight.copy(seeds[i]).next(
len(k)), list(co.values())),
numpy.array(delays)[k],
])
cli.append(a)
# Add the output to the two queues to communicate it with the main process
queue_cl.put(cli)
queue_v.put(vi)
def build_connector(indices, seeds, queue_cl, queue_v):
cli = []
vi = 0
for i in range(len(indices)):
samples = self.target.get_neuron_annotation(
indices[i], self.parameters.annotation_reference_name)
weights = self._obtain_weights(indices[i], seeds[i])
delays = self._obtain_delays(indices[i], seeds[i])
if self.parameters.num_samples == 0:
co = Counter(
sample_from_bin_distribution(weights, int(samples),
seeds[i]))
else:
assert self.parameters.num_samples > 2 * int(
samples), "%s: %d %d" % (
self.name,
self.parameters.num_samples,
2 * int(samples),
)
co = Counter(
sample_from_bin_distribution(
weights,
int(self.parameters.num_samples -
2 * int(samples)),
seeds[i],
))
vi = vi + numpy.sum(list(co.values()))
k = list(co.keys())
a = numpy.array([
k,
numpy.zeros(len(k)) + indices[i],
self.weight_scaler * numpy.multiply(
self.parameters.base_weight.copy(seeds[i]).next(
len(k)), list(co.values())),
numpy.array(delays)[k],
])
cli.append(a)
# Add the output to the two queues to communicate it with the main process
queue_cl.put(cli)
queue_v.put(vi)
def _connect(self):
cl = []
for i in numpy.nonzero(self.target.pop._mask_local)[0]:
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
co = Counter(sample_from_bin_distribution(weights, int(self.parameters.num_samples.next())))
cl.extend([(k,i,self.weight_scaler*self.parameters.base_weight.next()*co[k],delays[k]) for k in co.keys()])
method = self.sim.FromListConnector(cl)
if len(cl) > 0:
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
else:
logger.warning("%s(%s): empty projection - pyNN projection not created." % (self.name,self.__class__.__name__))
def connect(self):
cl = []
d={}
for i,(s,t,w,delay) in enumerate(self.connection_list):
if d.has_key(t):
d[t].append(i)
else:
d[t] = [i]
for k in d.keys():
w = [self.connection_list[i][2] for i in d[k]]
samples = sample_from_bin_distribution(w,self.parameters.num_samples)
a = [self.connection_list[d[k][s]] for s in samples]
cl.extend([(a,b,self.parameters.weight_factor/len(samples),de) for (a,b,c,de) in a])
method = self.sim.FromListConnector(cl)
self.proj = self.sim.Projection(self.source.pop, self.target.pop, method, synapse_dynamics=self.short_term_plasticity, label=self.name, rng=None, target=self.parameters.target_synapses)
def _connect(self):
# This is due to native synapses models (which we currently use as the short term synaptic plasticity model)
# do not apply the 1000 factor scaler as the pyNN synaptic models
wf = self.parameters.weight_factor * self.weight_scaler
weights = self.connection_matrix
delays = self.delay_matrix
cl = []
for i in xrange(0,self.target.pop.size):
co = Counter(sample_from_bin_distribution(weights[:,i].flatten(), int(self.parameters.num_samples)))
cl.extend([(int(k),int(i),wf*co[k]/self.parameters.num_samples,delays[k][i]) for k in co.keys()])
method = self.sim.FromListConnector(cl)
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
def _connect(self):
cl = []
for i in numpy.nonzero(self.target.pop._mask_local)[0]:
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
co = Counter(
sample_from_bin_distribution(weights,
self.parameters.num_samples))
cl.extend([(k, i, self.weight_scaler *
self.parameters.base_weight * co[k], delays[k])
for k in co.keys()])
method = self.sim.FromListConnector(cl)
if len(cl) > 0:
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
else:
logger.warning(
"%s(%s): empty projection - pyNN projection not created." %
(self.name, self.__class__.__name__))
def _determine_weights_and_delays(self,i):
weights = self._obtain_weights(i)
delays = self._obtain_delays(i)
co = Counter(sample_from_bin_distribution(weights, self.parameters.num_samples.next()))
return co.keys(),[self.weight_scaler*self.parameters.base_weight.next()*co[k] for k in co.keys()],[delays[k] for k in co.keys()]
