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())
for i in range(0, len(self.conn_list)):
conn = self.conn_list[i]
pre_atom = generateParameter(conn[0], i)
post_atom = generateParameter(conn[1], i)
weight = generateParameter(conn[2], i)
delay = generateParameter(conn[3], i) * delay_scale
id_lists[pre_atom].append(post_atom)
weight_lists[pre_atom].append(weight)
delay_lists[pre_atom].append(delay)
type_lists[pre_atom].append(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())
numIncomingAxons = prevertex.atoms
numTargetNeurons = postvertex.atoms
for _ in range(0, self.numSynapses):
source = int(random() * numIncomingAxons)
target = int(random() * numTargetNeurons)
weight = generateParameter(self.weights, target)
delay = generateParameter(self.weights, target) * delay_scale
id_lists[source].append(target)
weight_lists[source].append(weight)
delay_lists[source].append(delay)
type_lists[source].append(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())
for i in range(0, len(self.conn_list)):
conn = self.conn_list[i]
pre_atom = generateParameter(conn[0], i)
post_atom = generateParameter(conn[1], i)
weight = generateParameter(conn[2], i)
delay = generateParameter(conn[3], i) * delay_scale
id_lists[pre_atom].append(post_atom)
weight_lists[pre_atom].append(weight)
delay_lists[pre_atom].append(delay)
type_lists[pre_atom].append(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):
connection_list = list()
for pre_atom in range(0, prevertex.atoms):
delay = generateParameter(self.delays, pre_atom) * delay_scale
weight = generateParameter(self.weights, pre_atom)
connection_list.append(SynapseRowInfo([pre_atom], [weight],
[delay], [synapse_type]))
return SynapticList(connection_list)
def writePoissonParameters(self, spec, machineTimeStep, processor,
numNeurons):
"""
Generate Neuron Parameter data for Poisson spike sources (region 2):
"""
spec.comment("\nWriting Neuron Parameters for %d poisson sources:\n"
% numNeurons)
# Set the focus to the memory region 2 (neuron parameters):
spec.switchWriteFocus(region = POISSON_PARAMS_REGION)
# Write header info to the memory region:
# Write Key info for this core:
chipX, chipY, chipP = processor.get_coordinates()
populationIdentity = \
packet_conversions.get_key_from_coords(chipX, chipY, chipP)
spec.write(data = populationIdentity)
# Write the random seed (4 words), generated randomly!
if self.seed == None:
spec.write(data = numpy.random.randint(0x7FFFFFFF))
spec.write(data = numpy.random.randint(0x7FFFFFFF))
spec.write(data = numpy.random.randint(0x7FFFFFFF))
spec.write(data = numpy.random.randint(0x7FFFFFFF))
else:
spec.write(data = self.seed[0])
spec.write(data = self.seed[1])
spec.write(data = self.seed[2])
spec.write(data = self.seed[3])
# For each neuron, get the rate to work out if it is a slow
# or fast source
slow_sources = list()
fast_sources = list()
for i in range(0, numNeurons):
# Get the parameter values for source i:
rateVal = generateParameter(self.rate, i)
startVal = generateParameter(self.start, i)
endVal = generateParameter(self.duration, i) + startVal
# Decide if it is a fast or slow source and
spikesPerTick = (float(rateVal) * (machineTimeStep / 1000000.0))
if spikesPerTick <= SLOW_RATE_PER_TICK_CUTOFF:
slow_sources.append([i, rateVal, startVal, endVal])
else:
fast_sources.append([i, spikesPerTick, startVal, endVal])
# Write the numbers of each type of source
spec.write(data = len(slow_sources))
spec.write(data = len(fast_sources))
# Now write one struct for each slow source as follows
#
# typedef struct slow_spike_source_t
# {
# uint32_t neuron_id;
# uint32_t start_ticks;
# uint32_t end_ticks;
#
# accum mean_isi_ticks;
# accum time_to_spike_ticks;
# } slow_spike_source_t;
for (neuronId, rateVal, startVal, endVal) in slow_sources:
isiValScaled = int(float(1000000.0 / (rateVal * machineTimeStep))
* 32768.0)
startScaled = int(startVal * 1000.0 / machineTimeStep)
endScaled = int(endVal * 1000.0 / machineTimeStep)
spec.write(data = neuronId, sizeof='uint32')
spec.write(data = startScaled, sizeof='uint32')
spec.write(data = endScaled, sizeof='uint32')
spec.write(data = isiValScaled, sizeof='s1615')
spec.write(data = 0x0, sizeof='uint32')
# Now write
# typedef struct fast_spike_source_t
# {
# uint32_t neuron_id;
# uint32_t start_ticks;
# uint32_t end_ticks;
#
# unsigned long fract exp_minus_lambda;
# } fast_spike_source_t;
for (neuronId, spikesPerTick, startVal, endVal) in fast_sources:
exp_minus_lamda = exp(-1.0 * spikesPerTick)
exp_minus_lamda_scaled = int(exp_minus_lamda * float(0xFFFFFFFF))
startScaled = int(startVal * 1000.0 / machineTimeStep)
endScaled = int(endVal * 1000.0 / machineTimeStep)
spec.write(data = neuronId, sizeof='uint32')
spec.write(data = startScaled, sizeof='uint32')
spec.write(data = endScaled, sizeof='uint32')
spec.write(data = exp_minus_lamda_scaled, sizeof='u032')
return
def writePoissonParameters(self, spec, machineTimeStep, processor,
numNeurons):
"""
Generate Neuron Parameter data for Poisson spike sources (region 2):
"""
spec.comment("\nWriting Neuron Parameters for %d poisson sources:\n" %
numNeurons)
# Set the focus to the memory region 2 (neuron parameters):
spec.switchWriteFocus(region=POISSON_PARAMS_REGION)
# Write header info to the memory region:
# Write Key info for this core:
chipX, chipY, chipP = processor.get_coordinates()
populationIdentity = \
packet_conversions.get_key_from_coords(chipX, chipY, chipP)
spec.write(data=populationIdentity)
# Write the random seed (4 words), generated randomly!
if self.seed == None:
spec.write(data=numpy.random.randint(0x7FFFFFFF))
spec.write(data=numpy.random.randint(0x7FFFFFFF))
spec.write(data=numpy.random.randint(0x7FFFFFFF))
spec.write(data=numpy.random.randint(0x7FFFFFFF))
else:
spec.write(data=self.seed[0])
spec.write(data=self.seed[1])
spec.write(data=self.seed[2])
spec.write(data=self.seed[3])
# For each neuron, get the rate to work out if it is a slow
# or fast source
slow_sources = list()
fast_sources = list()
for i in range(0, numNeurons):
# Get the parameter values for source i:
rateVal = generateParameter(self.rate, i)
startVal = generateParameter(self.start, i)
endVal = generateParameter(self.duration, i) + startVal
# Decide if it is a fast or slow source and
spikesPerTick = (float(rateVal) * (machineTimeStep / 1000000.0))
if spikesPerTick <= SLOW_RATE_PER_TICK_CUTOFF:
slow_sources.append([i, rateVal, startVal, endVal])
else:
fast_sources.append([i, spikesPerTick, startVal, endVal])
# Write the numbers of each type of source
spec.write(data=len(slow_sources))
spec.write(data=len(fast_sources))
# Now write one struct for each slow source as follows
#
# typedef struct slow_spike_source_t
# {
# uint32_t neuron_id;
# uint32_t start_ticks;
# uint32_t end_ticks;
#
# accum mean_isi_ticks;
# accum time_to_spike_ticks;
# } slow_spike_source_t;
for (neuronId, rateVal, startVal, endVal) in slow_sources:
isiValScaled = int(
float(1000000.0 / (rateVal * machineTimeStep)) * 32768.0)
startScaled = int(startVal * 1000.0 / machineTimeStep)
endScaled = int(endVal * 1000.0 / machineTimeStep)
spec.write(data=neuronId, sizeof='uint32')
spec.write(data=startScaled, sizeof='uint32')
spec.write(data=endScaled, sizeof='uint32')
spec.write(data=isiValScaled, sizeof='s1615')
spec.write(data=0x0, sizeof='uint32')
# Now write
# typedef struct fast_spike_source_t
# {
# uint32_t neuron_id;
# uint32_t start_ticks;
# uint32_t end_ticks;
#
# unsigned long fract exp_minus_lambda;
# } fast_spike_source_t;
for (neuronId, spikesPerTick, startVal, endVal) in fast_sources:
exp_minus_lamda = exp(-1.0 * spikesPerTick)
exp_minus_lamda_scaled = int(exp_minus_lamda * float(0xFFFFFFFF))
startScaled = int(startVal * 1000.0 / machineTimeStep)
endScaled = int(endVal * 1000.0 / machineTimeStep)
spec.write(data=neuronId, sizeof='uint32')
spec.write(data=startScaled, sizeof='uint32')
spec.write(data=endScaled, sizeof='uint32')
spec.write(data=exp_minus_lamda_scaled, sizeof='u032')
return
