def __init__(self,
timing_dependence=None,
weight_dependence=None,
voltage_dependence=None,
dendritic_delay_fraction=1.0):
AbstractPlasticSynapseDynamics.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._timing_dependence = timing_dependence
self._weight_dependence = weight_dependence
self._dendritic_delay_fraction = float(dendritic_delay_fraction)
self._change_requires_mapping = True
if (self._dendritic_delay_fraction < 0.5
or self._dendritic_delay_fraction > 1.0):
raise NotImplementedError(
"dendritic_delay_fraction must be in the interval [0.5, 1.0]")
if self._timing_dependence is None or self._weight_dependence is None:
raise NotImplementedError(
"Both timing_dependence and weight_dependence must be"
"specified")
if voltage_dependence is not None:
raise NotImplementedError(
"Voltage dependence has not been implemented")
def __init__(self):
AbstractPopulationSettable.__init__(self)
def __init__(
self, n_neurons, binary, label, max_atoms_per_core,
machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, model_name, neuron_model, input_type,
synapse_type, threshold_type, additional_input=None,
constraints=None):
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractPartitionableVertex.__init__(
self, n_neurons, label, max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step, timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractMappable.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._v_buffer_max_size = config.getint(
"Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint(
"Buffers", "gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
# Set up synapse handling
self._synapse_manager = SynapticManager(
synapse_type, machine_time_step, ring_buffer_sigma,
spikes_per_second)
# Get buffering information for later use
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# bool for if state has changed.
self._change_requires_mapping = True
def __init__(self,
n_neurons,
binary,
label,
max_atoms_per_core,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
model_name,
neuron_model,
input_type,
synapse_type,
threshold_type,
additional_input=None,
constraints=None):
AbstractPartitionableVertex.__init__(self, n_neurons, label,
max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(self, machine_time_step,
timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
AbstractProvidesIncomingPartitionConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._incoming_spike_buffer_size = incoming_spike_buffer_size
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
self._v_buffer_max_size = config.getint("Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint("Buffers",
"gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = config.getint("Buffers",
"receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# Set up synapse handling
self._synapse_manager = SynapticManager(synapse_type,
machine_time_step,
ring_buffer_sigma,
spikes_per_second)
# bool for if state has changed.
self._change_requires_mapping = True
def __init__(self):
AbstractStaticSynapseDynamics.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._change_requires_mapping = True
def __init__(self):
AbstractPopulationSettable.__init__(self)
