def createNeurone(name, component_info, rng, parameters):
"""
Returns daetoolsComponent object based on the supplied daetoolsComponentInfo object.
There are some special cases which are handled individually such as:
* SpikeSourcePoisson
:param name: string
:param component_info: AL Component object
:param rng: numpy.random.RandomState object
:param parameters: python dictionary 'name' : value
:rtype: daetoolsComponent object
:raises: RuntimeError
"""
neurone = None
"""
ACHTUNG, ACHTUNG!!
We should handle components' with names that mean something. How can we know that
the spike_source_poisson component should be treated differently by a simulator?
The same stands for other types of components.
"""
if component_info.name == 'spike_source_poisson':
if 'rate' in parameters:
# Create daetools quantity and scale it to 'Hz'
rate = pyUnits.quantity(parameters['rate'][0], parameters['rate'][1]).scaleTo(pyUnits.Hz)
#print('spike_source_poisson.rate = %s' % rate)
else:
raise RuntimeError('The SpikeSourcePoisson component: must have [rate] parameter')
if 'duration' in parameters:
# Create daetools quantity and scale it to 's'
duration = pyUnits.quantity(parameters['duration'][0], parameters['duration'][1]).scaleTo(pyUnits.s)
#print('spike_source_poisson.duration = %s' % duration)
else:
raise RuntimeError('The SpikeSourcePoisson component: must have [duration] parameter')
if 't0' in parameters:
# Create daetools quantity and scale it to 's'
t0 = pyUnits.quantity(parameters['t0'][0], parameters['t0'][1]).scaleTo(pyUnits.s)
#print('spike_source_poisson.t0 = %s' % t0)
else:
t0 = 0.0
# Should be rate [Hz] * duration [s]
lambda_ = rate.value * duration.value
spiketimes = createPoissonSpikeTimes(rate, duration, t0, rng, lambda_, rng)
neurone = daetoolsSpikeSource(spiketimes, name, None, '')
else:
neurone = daetoolsComponent(component_info, fixObjectName(name), None, '')
neurone.Nitems = 1
neurone.initialize()
spike_event_port = neurone.getOutletEventPort()
neurone.on_spike_out_action = daetoolsOnSpikeOutAction(neurone, spike_event_port)
neurone.ON_EVENT(spike_event_port, userDefinedActions = [neurone.on_spike_out_action])
return neurone
def createConnections(self, cgi, source_population, target_population,
psr_weight_units, connrule_weight_units, connrule_delay_units):
"""
Iterates over ConnectionGeneratorInterface object and creates connections.
It connects source->target neurones and (optionally) sets weights and delays
:param cgi: ConnectionGenerator interface object
:rtype: None
:raises: RuntimeError
"""
count = 0
for connection in cgi:
size = len(connection)
if(size < 2):
raise RuntimeError('Not enough data in the explicit lists of connections')
source_index = int(connection[0])
target_index = int(connection[1])
weight = 0.0
delay = 0.0
parameters = []
# Weight and delay values are in units specified by the user in the UL component
# Weight will be scaled to the units in the PSR component, while delay to seconds
if cgi.arity == 1:
weight = pyUnits.quantity(float(connection[2]), connrule_weight_units)
elif cgi.arity >= 2:
weight = pyUnits.quantity(float(connection[2]), connrule_weight_units)
delay = pyUnits.quantity(float(connection[3]), connrule_delay_units)
source_neurone = source_population.getNeurone(source_index)
target_neurone = target_population.getNeurone(target_index)
synapse = self.getSynapse(target_index)
# Scale the delay to seconds
delay_in_seconds = delay.scaleTo(pyUnits.s).value
#print('delay_in_seconds = %f' % delay_in_seconds)
if delay_in_seconds < self.minimal_delay:
self.minimal_delay = delay_in_seconds
# Add the new item to the list of connected synapse event ports and connection delays.
# Here we cannot add an event port directly since it does not exist yet.
# Therefore, we add the synapse object and the index of the event port.
source_neurone.target_synapses.append( (synapse, synapse.Nitems, delay_in_seconds, target_neurone) )
synapse.Nitems += 1
# Weights cannot be set right now. Thus, we put them into an array.
# Also, the units must be scaled to those specified in the PSR component
synapse.synapse_weights.append( weight.scaleTo(psr_weight_units) )
count += 1
print('{0}: created {1} connections'.format(self.name, count))
def createNeurone(name, component_info, rng, parameters):
"""
Returns daetoolsComponent object based on the supplied daetoolsComponentInfo object.
There are some special cases which are handled individually such as:
* SpikeSourcePoisson
:param name: string
:param component_info: AL Component object
:param rng: numpy.random.RandomState object
:param parameters: python dictionary 'name' : value
:rtype: daetoolsComponent object
:raises: RuntimeError
"""
neurone = None
"""
ACHTUNG, ACHTUNG!!
We should handle components' with names that mean something. How can we know that
the spike_source_poisson component should be treated differently by a simulator?
The same stands for other types of components.
"""
if component_info.name == 'spike_source_poisson':
if 'rate' in parameters:
# Create daetools quantity and scale it to 'Hz'
rate = pyUnits.quantity(parameters['rate'][0],
parameters['rate'][1]).scaleTo(pyUnits.Hz)
#print('spike_source_poisson.rate = %s' % rate)
else:
raise RuntimeError(
'The SpikeSourcePoisson component: must have [rate] parameter')
if 'duration' in parameters:
# Create daetools quantity and scale it to 's'
duration = pyUnits.quantity(parameters['duration'][0],
parameters['duration'][1]).scaleTo(
pyUnits.s)
#print('spike_source_poisson.duration = %s' % duration)
else:
raise RuntimeError(
'The SpikeSourcePoisson component: must have [duration] parameter'
)
if 't0' in parameters:
# Create daetools quantity and scale it to 's'
t0 = pyUnits.quantity(parameters['t0'][0],
parameters['t0'][1]).scaleTo(pyUnits.s)
#print('spike_source_poisson.t0 = %s' % t0)
else:
t0 = 0.0
# Should be rate [Hz] * duration [s]
lambda_ = rate.value * duration.value
spiketimes = createPoissonSpikeTimes(rate, duration, t0, rng, lambda_,
rng)
neurone = daetoolsSpikeSource(spiketimes, name, None, '')
else:
neurone = daetoolsComponent(component_info, fixObjectName(name), None,
'')
neurone.Nitems = 1
neurone.initialize()
spike_event_port = neurone.getOutletEventPort()
neurone.on_spike_out_action = daetoolsOnSpikeOutAction(
neurone, spike_event_port)
neurone.ON_EVENT(spike_event_port,
userDefinedActions=[neurone.on_spike_out_action])
return neurone
def createConnections(self, cgi, source_population, target_population,
psr_weight_units, connrule_weight_units,
connrule_delay_units):
"""
Iterates over ConnectionGeneratorInterface object and creates connections.
It connects source->target neurones and (optionally) sets weights and delays
:param cgi: ConnectionGenerator interface object
:rtype: None
:raises: RuntimeError
"""
count = 0
for connection in cgi:
size = len(connection)
if (size < 2):
raise RuntimeError(
'Not enough data in the explicit lists of connections')
source_index = int(connection[0])
target_index = int(connection[1])
weight = 0.0
delay = 0.0
parameters = []
# Weight and delay values are in units specified by the user in the UL component
# Weight will be scaled to the units in the PSR component, while delay to seconds
if cgi.arity == 1:
weight = pyUnits.quantity(float(connection[2]),
connrule_weight_units)
elif cgi.arity >= 2:
weight = pyUnits.quantity(float(connection[2]),
connrule_weight_units)
delay = pyUnits.quantity(float(connection[3]),
connrule_delay_units)
source_neurone = source_population.getNeurone(source_index)
target_neurone = target_population.getNeurone(target_index)
synapse = self.getSynapse(target_index)
# Scale the delay to seconds
delay_in_seconds = delay.scaleTo(pyUnits.s).value
#print('delay_in_seconds = %f' % delay_in_seconds)
if delay_in_seconds < self.minimal_delay:
self.minimal_delay = delay_in_seconds
# Add the new item to the list of connected synapse event ports and connection delays.
# Here we cannot add an event port directly since it does not exist yet.
# Therefore, we add the synapse object and the index of the event port.
source_neurone.target_synapses.append(
(synapse, synapse.Nitems, delay_in_seconds, target_neurone))
synapse.Nitems += 1
# Weights cannot be set right now. Thus, we put them into an array.
# Also, the units must be scaled to those specified in the PSR component
synapse.synapse_weights.append(weight.scaleTo(psr_weight_units))
count += 1
print('{0}: created {1} connections'.format(self.name, count))
