def __init__(self, n_neurons, machine_time_step, timescale_factor, constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0, duration=None, seed=None): """ Creates a new SpikeSourcePoisson Object. """ AbstractPartitionableVertex.__init__( self, n_atoms=n_neurons, label=label, constraints=constraints, max_atoms_per_core=self._model_based_max_atoms_per_core) AbstractDataSpecableVertex.__init__( self, machine_time_step=machine_time_step, timescale_factor=timescale_factor) AbstractSpikeRecordable.__init__(self) # Store the parameters self._rate = rate self._start = start self._duration = duration self._rng = numpy.random.RandomState(seed) # Prepare for recording, and to get spikes self._spike_recorder = SpikeRecorder(machine_time_step) self._outgoing_edge_key_restrictor = \ OutgoingEdgeSameContiguousKeysRestrictor()
def __init__( self, n_neurons, machine_time_step, timescale_factor, constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0, duration=None, seed=None): AbstractPartitionableVertex.__init__( self, n_atoms=n_neurons, label=label, constraints=constraints, max_atoms_per_core=self._model_based_max_atoms_per_core) AbstractDataSpecableVertex.__init__( self, machine_time_step=machine_time_step, timescale_factor=timescale_factor) AbstractSpikeRecordable.__init__(self) ReceiveBuffersToHostBasicImpl.__init__(self) AbstractProvidesOutgoingEdgeConstraints.__init__(self) PopulationSettableChangeRequiresMapping.__init__(self) # Store the parameters self._rate = rate self._start = start self._duration = duration self._rng = numpy.random.RandomState(seed) # Prepare for recording, and to get spikes self._spike_recorder = SpikeRecorder(machine_time_step) self._spike_buffer_max_size = config.getint( "Buffers", "spike_buffer_size") self._buffer_size_before_receive = config.getint( "Buffers", "buffer_size_before_receive") self._time_between_requests = config.getint( "Buffers", "time_between_requests")
def __init__(self, n_neurons, constraints=none_pynn_default_parameters['constraints'], label=none_pynn_default_parameters['label'], rate=default_parameters['rate'], start=default_parameters['start'], duration=default_parameters['duration'], seed=none_pynn_default_parameters['seed']): ApplicationVertex.__init__(self, label, constraints, self._model_based_max_atoms_per_core) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingPartitionConstraints.__init__(self) AbstractChangableAfterRun.__init__(self) SimplePopulationSettable.__init__(self) ProvidesKeyToAtomMappingImpl.__init__(self) config = globals_variables.get_simulator().config # atoms params self._n_atoms = n_neurons self._seed = None # check for changes parameters self._change_requires_mapping = True self._change_requires_neuron_parameters_reload = False # Store the parameters self._rate = utility_calls.convert_param_to_numpy(rate, n_neurons) self._start = utility_calls.convert_param_to_numpy(start, n_neurons) self._duration = utility_calls.convert_param_to_numpy( duration, n_neurons) self._time_to_spike = utility_calls.convert_param_to_numpy( 0, n_neurons) self._rng = numpy.random.RandomState(seed) self._machine_time_step = None # Prepare for recording, and to get spikes self._spike_recorder = MultiSpikeRecorder() self._time_between_requests = config.getint("Buffers", "time_between_requests") self._receive_buffer_host = config.get("Buffers", "receive_buffer_host") self._receive_buffer_port = helpful_functions.read_config_int( config, "Buffers", "receive_buffer_port") self._minimum_buffer_sdram = config.getint("Buffers", "minimum_buffer_sdram") self._using_auto_pause_and_resume = config.getboolean( "Buffers", "use_auto_pause_and_resume") spike_buffer_max_size = 0 self._buffer_size_before_receive = None if config.getboolean("Buffers", "enable_buffered_recording"): spike_buffer_max_size = config.getint("Buffers", "spike_buffer_size") self._buffer_size_before_receive = config.getint( "Buffers", "buffer_size_before_receive") self._maximum_sdram_for_buffering = [spike_buffer_max_size]
def __init__( self, n_neurons, spike_times, machine_time_step, timescale_factor, port=None, tag=None, ip_address=None, board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=None, space_before_notification=640, constraints=None, label="SpikeSourceArray"): if ip_address is None: ip_address = config.get("Buffers", "receive_buffer_host") if port is None: port = config.getint("Buffers", "receive_buffer_port") AbstractDataSpecableVertex.__init__( self, machine_time_step=machine_time_step, timescale_factor=timescale_factor) AbstractPartitionableVertex.__init__( self, n_atoms=n_neurons, label=label, max_atoms_per_core=self._model_based_max_atoms_per_core, constraints=constraints) AbstractSpikeRecordable.__init__(self) self._spike_times = spike_times self._max_on_chip_memory_usage_for_spikes = \ max_on_chip_memory_usage_for_spikes_in_bytes self._space_before_notification = space_before_notification self.add_constraint(TagAllocatorRequireIptagConstraint( ip_address, port, strip_sdp=True, board_address=board_address, tag_id=tag)) if self._max_on_chip_memory_usage_for_spikes is None: self._max_on_chip_memory_usage_for_spikes = \ front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP # check the values do not conflict with chip memory limit if self._max_on_chip_memory_usage_for_spikes < 0: raise ConfigurationException( "The memory usage on chip is either beyond what is supportable" " on the spinnaker board being supported or you have requested" " a negative value for a memory usage. Please correct and" " try again") if (self._max_on_chip_memory_usage_for_spikes < self._space_before_notification): self._space_before_notification =\ self._max_on_chip_memory_usage_for_spikes # Keep track of any previously generated buffers self._send_buffers = dict() self._spike_recording_region_size = None # handle recording self._spike_recorder = EIEIOSpikeRecorder(machine_time_step) #handle outgoing constraints self._outgoing_edge_key_restrictor = \ OutgoingEdgeSameContiguousKeysRestrictor()
def __init__( self, n_neurons, machine_time_step, timescale_factor, constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0, duration=None, seed=None): AbstractPartitionableVertex.__init__( self, n_neurons, label, self._model_based_max_atoms_per_core, constraints) AbstractDataSpecableVertex.__init__( self, machine_time_step=machine_time_step, timescale_factor=timescale_factor) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingPartitionConstraints.__init__(self) PopulationSettableChangeRequiresMapping.__init__(self) # Store the parameters self._rate = utility_calls.convert_param_to_numpy(rate, n_neurons) self._start = utility_calls.convert_param_to_numpy(start, n_neurons) self._duration = utility_calls.convert_param_to_numpy( duration, n_neurons) self._rng = numpy.random.RandomState(seed) # Prepare for recording, and to get spikes self._spike_recorder = MultiSpikeRecorder(machine_time_step) self._spike_buffer_max_size = config.getint( "Buffers", "spike_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._enable_buffered_recording = config.getboolean( "Buffers", "enable_buffered_recording") self._receive_buffer_host = config.get( "Buffers", "receive_buffer_host") self._receive_buffer_port = config.getint( "Buffers", "receive_buffer_port") self._minimum_buffer_sdram = config.getint( "Buffers", "minimum_buffer_sdram") self._using_auto_pause_and_resume = config.getboolean( "Buffers", "use_auto_pause_and_resume")
def __init__( self, n_neurons, spike_times, machine_time_step, timescale_factor, port=None, tag=None, ip_address=None, board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=( constants.SPIKE_BUFFER_SIZE_BUFFERING_IN), space_before_notification=640, constraints=None, label="SpikeSourceArray", spike_recorder_buffer_size=( constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT), buffer_size_before_receive=( constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)): self._ip_address = ip_address if ip_address is None: self._ip_address = config.get("Buffers", "receive_buffer_host") self._port = port if port is None: self._port = config.getint("Buffers", "receive_buffer_port") ReverseIpTagMultiCastSource.__init__( self, n_keys=n_neurons, machine_time_step=machine_time_step, timescale_factor=timescale_factor, label=label, constraints=constraints, max_atoms_per_core=(SpikeSourceArray. _model_based_max_atoms_per_core), board_address=board_address, receive_port=None, receive_sdp_port=None, receive_tag=None, virtual_key=None, prefix=None, prefix_type=None, check_keys=False, send_buffer_times=spike_times, send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes, send_buffer_space_before_notify=space_before_notification, send_buffer_notification_ip_address=self._ip_address, send_buffer_notification_port=self._port, send_buffer_notification_tag=tag) AbstractSpikeRecordable.__init__(self) # handle recording self._spike_recorder = EIEIOSpikeRecorder(machine_time_step) self._spike_recorder_buffer_size = spike_recorder_buffer_size self._buffer_size_before_receive = buffer_size_before_receive
def __init__( self, n_neurons, machine_time_step, timescale_factor, constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0, duration=None, seed=None): AbstractPartitionableVertex.__init__( self, n_neurons, label, self._model_based_max_atoms_per_core, constraints) AbstractDataSpecableVertex.__init__( self, machine_time_step=machine_time_step, timescale_factor=timescale_factor) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingPartitionConstraints.__init__(self) PopulationSettableChangeRequiresMapping.__init__(self) # Store the parameters self._rate = rate self._start = start self._duration = duration self._rng = numpy.random.RandomState(seed) # Prepare for recording, and to get spikes self._spike_recorder = SpikeRecorder(machine_time_step) self._spike_buffer_max_size = config.getint( "Buffers", "spike_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._enable_buffered_recording = config.getboolean( "Buffers", "enable_buffered_recording") self._receive_buffer_host = config.get( "Buffers", "receive_buffer_host") self._receive_buffer_port = config.getint( "Buffers", "receive_buffer_port") self._minimum_buffer_sdram = config.getint( "Buffers", "minimum_buffer_sdram") self._using_auto_pause_and_resume = config.getboolean( "Buffers", "use_auto_pause_and_resume")
def __init__( self, n_neurons, machine_time_step, timescale_factor, spike_times=None, port=None, tag=None, ip_address=None, board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=(constants.SPIKE_BUFFER_SIZE_BUFFERING_IN), space_before_notification=640, constraints=None, label="SpikeSourceArray", spike_recorder_buffer_size=(constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT), buffer_size_before_receive=(constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE), ): self._ip_address = ip_address if ip_address is None: self._ip_address = config.get("Buffers", "receive_buffer_host") self._port = port if port is None: self._port = config.getint("Buffers", "receive_buffer_port") if spike_times is None: spike_times = [] ReverseIpTagMultiCastSource.__init__( self, n_keys=n_neurons, machine_time_step=machine_time_step, timescale_factor=timescale_factor, label=label, constraints=constraints, max_atoms_per_core=(SpikeSourceArray._model_based_max_atoms_per_core), board_address=board_address, receive_port=None, receive_sdp_port=None, receive_tag=None, virtual_key=None, prefix=None, prefix_type=None, check_keys=False, send_buffer_times=spike_times, send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes, send_buffer_space_before_notify=space_before_notification, send_buffer_notification_ip_address=self._ip_address, send_buffer_notification_port=self._port, send_buffer_notification_tag=tag, ) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingEdgeConstraints.__init__(self) SimplePopulationSettable.__init__(self) AbstractMappable.__init__(self) AbstractHasFirstMachineTimeStep.__init__(self) # handle recording self._spike_recorder = EIEIOSpikeRecorder(machine_time_step) self._spike_recorder_buffer_size = spike_recorder_buffer_size self._buffer_size_before_receive = buffer_size_before_receive # Keep track of any previously generated buffers self._send_buffers = dict() self._spike_recording_region_size = None self._partitioned_vertices = list() self._partitioned_vertices_current_max_buffer_size = dict() # used for reset and rerun self._requires_mapping = True self._last_runtime_position = 0 self._max_on_chip_memory_usage_for_spikes = max_on_chip_memory_usage_for_spikes_in_bytes self._space_before_notification = space_before_notification if self._max_on_chip_memory_usage_for_spikes is None: self._max_on_chip_memory_usage_for_spikes = front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP # check the values do not conflict with chip memory limit if self._max_on_chip_memory_usage_for_spikes < 0: raise exceptions.ConfigurationException( "The memory usage on chip is either beyond what is supportable" " on the spinnaker board being supported or you have requested" " a negative value for a memory usage. Please correct and" " try again" ) if self._max_on_chip_memory_usage_for_spikes < self._space_before_notification: self._space_before_notification = self._max_on_chip_memory_usage_for_spikes
def __init__(self, n_neurons, spike_times=default_parameters['spike_times'], port=none_pynn_default_parameters['port'], tag=none_pynn_default_parameters['tag'], ip_address=none_pynn_default_parameters['ip_address'], board_address=none_pynn_default_parameters['board_address'], max_on_chip_memory_usage_for_spikes_in_bytes=DEFAULT1, space_before_notification=none_pynn_default_parameters[ 'space_before_notification'], constraints=none_pynn_default_parameters['constraints'], label=none_pynn_default_parameters['label'], spike_recorder_buffer_size=none_pynn_default_parameters[ 'spike_recorder_buffer_size'], buffer_size_before_receive=none_pynn_default_parameters[ 'buffer_size_before_receive']): config = globals_variables.get_simulator().config self._ip_address = ip_address if ip_address is None: self._ip_address = config.get("Buffers", "receive_buffer_host") self._port = port if port is None: self._port = helpful_functions.read_config_int( config, "Buffers", "receive_buffer_port") if spike_times is None: spike_times = [] ReverseIpTagMultiCastSource.__init__( self, n_keys=n_neurons, label=label, constraints=constraints, max_atoms_per_core=( SpikeSourceArray._model_based_max_atoms_per_core), board_address=board_address, receive_port=None, receive_tag=None, virtual_key=None, prefix=None, prefix_type=None, check_keys=False, send_buffer_times=spike_times, send_buffer_partition_id=constants.SPIKE_PARTITION_ID, send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes, send_buffer_space_before_notify=space_before_notification, buffer_notification_ip_address=self._ip_address, buffer_notification_port=self._port, buffer_notification_tag=tag) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingPartitionConstraints.__init__(self) SimplePopulationSettable.__init__(self) AbstractChangableAfterRun.__init__(self) ProvidesKeyToAtomMappingImpl.__init__(self) # handle recording self._spike_recorder = EIEIOSpikeRecorder() self._spike_recorder_buffer_size = spike_recorder_buffer_size self._buffer_size_before_receive = buffer_size_before_receive # Keep track of any previously generated buffers self._send_buffers = dict() self._spike_recording_region_size = None self._machine_vertices = list() # used for reset and rerun self._requires_mapping = True self._last_runtime_position = 0 self._max_on_chip_memory_usage_for_spikes = \ max_on_chip_memory_usage_for_spikes_in_bytes self._space_before_notification = space_before_notification if self._max_on_chip_memory_usage_for_spikes is None: self._max_on_chip_memory_usage_for_spikes = \ front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP # check the values do not conflict with chip memory limit if self._max_on_chip_memory_usage_for_spikes < 0: raise exceptions.ConfigurationException( "The memory usage on chip is either beyond what is supportable" " on the spinnaker board being supported or you have requested" " a negative value for a memory usage. Please correct and" " try again") if (self._max_on_chip_memory_usage_for_spikes < self._space_before_notification): self._space_before_notification =\ self._max_on_chip_memory_usage_for_spikes
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, n_neurons, machine_time_step, timescale_factor, spike_times=None, port=None, tag=None, ip_address=None, board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=( constants.SPIKE_BUFFER_SIZE_BUFFERING_IN), space_before_notification=640, constraints=None, label="SpikeSourceArray", spike_recorder_buffer_size=( constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT), buffer_size_before_receive=( constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)): self._ip_address = ip_address if ip_address is None: self._ip_address = config.get("Buffers", "receive_buffer_host") self._port = port if port is None: self._port = config.getint("Buffers", "receive_buffer_port") if spike_times is None: spike_times = [] self._minimum_sdram_for_buffering = config.getint( "Buffers", "minimum_buffer_sdram") self._using_auto_pause_and_resume = config.getboolean( "Buffers", "use_auto_pause_and_resume") ReverseIpTagMultiCastSource.__init__( self, n_keys=n_neurons, machine_time_step=machine_time_step, timescale_factor=timescale_factor, label=label, constraints=constraints, max_atoms_per_core=(SpikeSourceArray. _model_based_max_atoms_per_core), board_address=board_address, receive_port=None, receive_sdp_port=None, receive_tag=None, virtual_key=None, prefix=None, prefix_type=None, check_keys=False, send_buffer_times=spike_times, send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes, send_buffer_space_before_notify=space_before_notification, send_buffer_notification_ip_address=self._ip_address, send_buffer_notification_port=self._port, send_buffer_notification_tag=tag) AbstractSpikeRecordable.__init__(self) AbstractProvidesOutgoingPartitionConstraints.__init__(self) SimplePopulationSettable.__init__(self) AbstractChangableAfterRun.__init__(self) AbstractHasFirstMachineTimeStep.__init__(self) # handle recording self._spike_recorder = EIEIOSpikeRecorder(machine_time_step) self._spike_recorder_buffer_size = spike_recorder_buffer_size self._buffer_size_before_receive = buffer_size_before_receive # Keep track of any previously generated buffers self._send_buffers = dict() self._spike_recording_region_size = None self._partitioned_vertices = list() self._partitioned_vertices_current_max_buffer_size = dict() # used for reset and rerun self._requires_mapping = True self._last_runtime_position = 0 self._max_on_chip_memory_usage_for_spikes = \ max_on_chip_memory_usage_for_spikes_in_bytes self._space_before_notification = space_before_notification if self._max_on_chip_memory_usage_for_spikes is None: self._max_on_chip_memory_usage_for_spikes = \ front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP # check the values do not conflict with chip memory limit if self._max_on_chip_memory_usage_for_spikes < 0: raise exceptions.ConfigurationException( "The memory usage on chip is either beyond what is supportable" " on the spinnaker board being supported or you have requested" " a negative value for a memory usage. Please correct and" " try again") if (self._max_on_chip_memory_usage_for_spikes < self._space_before_notification): self._space_before_notification =\ self._max_on_chip_memory_usage_for_spikes