spynnaker.pyNN.models.neural_projections package

Subpackages

• spynnaker.pyNN.models.neural_projections.connectors package
  • Module contents
    • AbstractConnector
      • AbstractConnector.NUMPY_SYNAPSES_DTYPE
      • AbstractConnector.connect()
      • AbstractConnector.get_connected_vertices()
      • AbstractConnector.get_delay_maximum()
      • AbstractConnector.get_delay_minimum()
      • AbstractConnector.get_delay_variance()
      • AbstractConnector.get_n_connections_from_pre_vertex_maximum()
      • AbstractConnector.get_n_connections_to_post_vertex_maximum()
      • AbstractConnector.get_provenance_data()
      • AbstractConnector.get_weight_maximum()
      • AbstractConnector.get_weight_mean()
      • AbstractConnector.get_weight_variance()
      • AbstractConnector.safe
      • AbstractConnector.set_projection_information()
      • AbstractConnector.set_space()
      • AbstractConnector.space
      • AbstractConnector.synapse_info
      • AbstractConnector.use_direct_matrix()
      • AbstractConnector.validate_connection()
      • AbstractConnector.verbose
    • AbstractGenerateConnectorOnHost
      • AbstractGenerateConnectorOnHost.create_synaptic_block()
    • AbstractGenerateConnectorOnMachine
      • AbstractGenerateConnectorOnMachine.gen_connector_id
      • AbstractGenerateConnectorOnMachine.gen_connector_params()
      • AbstractGenerateConnectorOnMachine.gen_connector_params_size_in_bytes
      • AbstractGenerateConnectorOnMachine.gen_delay_params()
      • AbstractGenerateConnectorOnMachine.gen_delay_params_size_in_bytes()
      • AbstractGenerateConnectorOnMachine.gen_delays_id()
      • AbstractGenerateConnectorOnMachine.gen_weight_params_size_in_bytes()
      • AbstractGenerateConnectorOnMachine.gen_weights_id()
      • AbstractGenerateConnectorOnMachine.gen_weights_params()
      • AbstractGenerateConnectorOnMachine.generate_on_machine()
      • AbstractGenerateConnectorOnMachine.validate_connection()
    • AllToAllConnector
      • AllToAllConnector.allow_self_connections
      • AllToAllConnector.create_synaptic_block()
      • AllToAllConnector.gen_connector_id
      • AllToAllConnector.gen_connector_params()
      • AllToAllConnector.gen_connector_params_size_in_bytes
      • AllToAllConnector.get_delay_maximum()
      • AllToAllConnector.get_delay_minimum()
      • AllToAllConnector.get_n_connections_from_pre_vertex_maximum()
      • AllToAllConnector.get_n_connections_to_post_vertex_maximum()
      • AllToAllConnector.get_weight_maximum()
    • ArrayConnector
      • ArrayConnector.create_synaptic_block()
      • ArrayConnector.get_delay_maximum()
      • ArrayConnector.get_delay_minimum()
      • ArrayConnector.get_n_connections_from_pre_vertex_maximum()
      • ArrayConnector.get_n_connections_to_post_vertex_maximum()
      • ArrayConnector.get_weight_maximum()
    • CSAConnector
      • CSAConnector.create_synaptic_block()
      • CSAConnector.get_delay_maximum()
      • CSAConnector.get_delay_minimum()
      • CSAConnector.get_n_connections_from_pre_vertex_maximum()
      • CSAConnector.get_n_connections_to_post_vertex_maximum()
      • CSAConnector.get_weight_maximum()
      • CSAConnector.show_connection_set()
    • ConvolutionConnector
      • ConvolutionConnector.get_connected_vertices()
      • ConvolutionConnector.get_delay_maximum()
      • ConvolutionConnector.get_delay_minimum()
      • ConvolutionConnector.get_encoded_kernel_weights()
      • ConvolutionConnector.get_local_only_data()
      • ConvolutionConnector.get_max_n_incoming_slices()
      • ConvolutionConnector.get_n_connections_from_pre_vertex_maximum()
      • ConvolutionConnector.get_n_connections_to_post_vertex_maximum()
      • ConvolutionConnector.get_post_shape()
      • ConvolutionConnector.get_weight_maximum()
      • ConvolutionConnector.kernel_n_bytes
      • ConvolutionConnector.kernel_n_weights
      • ConvolutionConnector.kernel_weights
      • ConvolutionConnector.negative_receptor_type
      • ConvolutionConnector.parameters_n_bytes
      • ConvolutionConnector.positive_receptor_type
      • ConvolutionConnector.validate_connection()
    • DistanceDependentProbabilityConnector
      • DistanceDependentProbabilityConnector.allow_self_connections
      • DistanceDependentProbabilityConnector.create_synaptic_block()
      • DistanceDependentProbabilityConnector.d_expression
      • DistanceDependentProbabilityConnector.get_delay_maximum()
      • DistanceDependentProbabilityConnector.get_delay_minimum()
      • DistanceDependentProbabilityConnector.get_n_connections_from_pre_vertex_maximum()
      • DistanceDependentProbabilityConnector.get_n_connections_to_post_vertex_maximum()
      • DistanceDependentProbabilityConnector.get_weight_maximum()
      • DistanceDependentProbabilityConnector.set_projection_information()
    • FixedNumberPostConnector
      • FixedNumberPostConnector.allow_self_connections
      • FixedNumberPostConnector.create_synaptic_block()
      • FixedNumberPostConnector.gen_connector_id
      • FixedNumberPostConnector.gen_connector_params()
      • FixedNumberPostConnector.gen_connector_params_size_in_bytes
      • FixedNumberPostConnector.get_delay_maximum()
      • FixedNumberPostConnector.get_delay_minimum()
      • FixedNumberPostConnector.get_n_connections_from_pre_vertex_maximum()
      • FixedNumberPostConnector.get_n_connections_to_post_vertex_maximum()
      • FixedNumberPostConnector.get_weight_maximum()
      • FixedNumberPostConnector.set_projection_information()
      • FixedNumberPostConnector.validate_connection()
    • FixedNumberPreConnector
      • FixedNumberPreConnector.allow_self_connections
      • FixedNumberPreConnector.create_synaptic_block()
      • FixedNumberPreConnector.gen_connector_id
      • FixedNumberPreConnector.gen_connector_params()
      • FixedNumberPreConnector.gen_connector_params_size_in_bytes
      • FixedNumberPreConnector.get_delay_maximum()
      • FixedNumberPreConnector.get_delay_minimum()
      • FixedNumberPreConnector.get_n_connections_from_pre_vertex_maximum()
      • FixedNumberPreConnector.get_n_connections_to_post_vertex_maximum()
      • FixedNumberPreConnector.get_weight_maximum()
      • FixedNumberPreConnector.set_projection_information()
      • FixedNumberPreConnector.validate_connection()
    • FixedProbabilityConnector
      • FixedProbabilityConnector.create_synaptic_block()
      • FixedProbabilityConnector.gen_connector_id
      • FixedProbabilityConnector.gen_connector_params()
      • FixedProbabilityConnector.gen_connector_params_size_in_bytes
      • FixedProbabilityConnector.get_delay_maximum()
      • FixedProbabilityConnector.get_delay_minimum()
      • FixedProbabilityConnector.get_n_connections_from_pre_vertex_maximum()
      • FixedProbabilityConnector.get_n_connections_to_post_vertex_maximum()
      • FixedProbabilityConnector.get_weight_maximum()
      • FixedProbabilityConnector.p_connect
      • FixedProbabilityConnector.validate_connection()
    • FromFileConnector
      • FromFileConnector.get_reader()
    • FromListConnector
      • FromListConnector.column_names
      • FromListConnector.conn_list
      • FromListConnector.create_synaptic_block()
      • FromListConnector.get_connected_vertices()
      • FromListConnector.get_delay_maximum()
      • FromListConnector.get_delay_minimum()
      • FromListConnector.get_delay_variance()
      • FromListConnector.get_extra_parameter_names()
      • FromListConnector.get_extra_parameters()
      • FromListConnector.get_n_connections_from_pre_vertex_maximum()
      • FromListConnector.get_n_connections_to_post_vertex_maximum()
      • FromListConnector.get_weight_maximum()
      • FromListConnector.get_weight_mean()
      • FromListConnector.get_weight_variance()
    • IndexBasedProbabilityConnector
      • IndexBasedProbabilityConnector.allow_self_connections
      • IndexBasedProbabilityConnector.create_synaptic_block()
      • IndexBasedProbabilityConnector.get_delay_maximum()
      • IndexBasedProbabilityConnector.get_delay_minimum()
      • IndexBasedProbabilityConnector.get_n_connections_from_pre_vertex_maximum()
      • IndexBasedProbabilityConnector.get_n_connections_to_post_vertex_maximum()
      • IndexBasedProbabilityConnector.get_weight_maximum()
      • IndexBasedProbabilityConnector.index_expression
    • KernelConnector
      • KernelConnector.create_synaptic_block()
      • KernelConnector.gen_connector_id
      • KernelConnector.gen_connector_params()
      • KernelConnector.gen_connector_params_size_in_bytes
      • KernelConnector.get_connected_vertices()
      • KernelConnector.get_delay_maximum()
      • KernelConnector.get_delay_minimum()
      • KernelConnector.get_delay_variance()
      • KernelConnector.get_n_connections_from_pre_vertex_maximum()
      • KernelConnector.get_n_connections_to_post_vertex_maximum()
      • KernelConnector.get_weight_maximum()
      • KernelConnector.get_weight_mean()
      • KernelConnector.get_weight_variance()
    • MultapseConnector
      • MultapseConnector.create_synaptic_block()
      • MultapseConnector.gen_connector_id
      • MultapseConnector.gen_connector_params()
      • MultapseConnector.gen_connector_params_size_in_bytes
      • MultapseConnector.get_delay_maximum()
      • MultapseConnector.get_delay_minimum()
      • MultapseConnector.get_n_connections_from_pre_vertex_maximum()
      • MultapseConnector.get_n_connections_to_post_vertex_maximum()
      • MultapseConnector.get_rng_next()
      • MultapseConnector.get_weight_maximum()
      • MultapseConnector.set_projection_information()
      • MultapseConnector.validate_connection()
    • OneToOneConnector
      • OneToOneConnector.create_synaptic_block()
      • OneToOneConnector.gen_connector_id
      • OneToOneConnector.gen_connector_params()
      • OneToOneConnector.gen_connector_params_size_in_bytes
      • OneToOneConnector.get_connected_vertices()
      • OneToOneConnector.get_delay_maximum()
      • OneToOneConnector.get_delay_minimum()
      • OneToOneConnector.get_n_connections_from_pre_vertex_maximum()
      • OneToOneConnector.get_n_connections_to_post_vertex_maximum()
      • OneToOneConnector.get_weight_maximum()
      • OneToOneConnector.use_direct_matrix()
    • PoolDenseConnector
      • PoolDenseConnector.get_delay_maximum()
      • PoolDenseConnector.get_delay_minimum()
      • PoolDenseConnector.get_n_connections_from_pre_vertex_maximum()
      • PoolDenseConnector.get_n_connections_to_post_vertex_maximum()
      • PoolDenseConnector.get_post_pool_shape()
      • PoolDenseConnector.get_weight_maximum()
      • PoolDenseConnector.local_only_n_bytes()
      • PoolDenseConnector.negative_receptor_type
      • PoolDenseConnector.positive_receptor_type
      • PoolDenseConnector.validate_connection()
      • PoolDenseConnector.weights
      • PoolDenseConnector.write_local_only_data()
    • SmallWorldConnector
      • SmallWorldConnector.create_synaptic_block()
      • SmallWorldConnector.get_delay_maximum()
      • SmallWorldConnector.get_delay_minimum()
      • SmallWorldConnector.get_n_connections_from_pre_vertex_maximum()
      • SmallWorldConnector.get_n_connections_to_post_vertex_maximum()
      • SmallWorldConnector.get_weight_maximum()
      • SmallWorldConnector.set_projection_information()

Module contents

class spynnaker.pyNN.models.neural_projections.DelayAfferentApplicationEdge(pre_vertex, delay_vertex, label=None)

Bases: ApplicationEdge

Parameters:

• pre_vertex (AbstractPopulationVertex) –

• delay_vertex (DelayExtensionVertex) –

• label (str) –

class spynnaker.pyNN.models.neural_projections.DelayedApplicationEdge(pre_vertex, post_vertex, synapse_information, undelayed_edge, label=None)

Bases: ApplicationEdge

Parameters:

• pre_vertex (DelayExtensionVertex) – The delay extension at the start of the edge

• post_vertex (AbstractPopulationVertex) – The target of the synapses

• synapse_information (SynapseInformation or iterable(SynapseInformation)) – The synapse information on this edge

• undelayed_edge (ProjectionApplicationEdge) – The edge that is used for projections without extended delays

• label (str) – The edge label

add_synapse_information(synapse_information)

Parameters:

synapse_information (SynapseInformation) –

property synapse_information

Return type:

list(SynapseInformation)

property undelayed_edge

The edge for projections without extended delays.

Return type:

ProjectionApplicationEdge

class spynnaker.pyNN.models.neural_projections.ProjectionApplicationEdge(pre_vertex, post_vertex, synapse_information, label=None)

Bases: ApplicationEdge, AbstractProvidesLocalProvenanceData

An edge which terminates on an AbstractPopulationVertex.

Parameters:

• pre_vertex (AbstractPopulationVertex) –

• post_vertex (AbstractPopulationVertex) –

• synapse_information (SynapseInformation) – The synapse information on this edge

• label (str) –

add_synapse_information(synapse_information)

Parameters:

synapse_information (SynapseInformation) –

property delay_edge

Settable.

Return type:

DelayedApplicationEdge or None

get_local_provenance_data()

Get provenance data items and store them in the provenance DB.

property is_neuromodulation

Whether this edge is providing neuromodulation.

Return type:

bool

property n_delay_stages

Return type:

int

property synapse_information

Return type:

list(SynapseInformation)

class spynnaker.pyNN.models.neural_projections.SynapseInformation(connector, pre_population, post_population, prepop_is_view, postpop_is_view, synapse_dynamics, synapse_type, receptor_type, synapse_type_from_dynamics, weights=None, delays=None)

Bases: object

Contains the synapse information including the connector, synapse type and synapse dynamics.

Parameters:

• connector (AbstractConnector) – The connector connected to the synapse

• pre_population (Population or PopulationView) – The population sending spikes to the synapse

• post_population (Population or PopulationView) – The population hosting the synapse

• prepop_is_view (bool) – Whether the pre_population is a view

• postpop_is_view (bool) – Whether the post_population is a view

• synapse_dynamics (AbstractSynapseDynamics) – The dynamic behaviour of the synapse

• synapse_type (int) – The type of the synapse

• receptor_type (str) – Description of the receptor (e.g. excitatory)

• synapse_type_from_dynamics (bool) – Whether the synapse type came from synapse dynamics

• weights (float or list(float) or ndarray(float) or None) – The synaptic weights

• delays (float or list(float) or ndarray(float) or None) – The total synaptic delays

add_pre_run_connection_holder(pre_run_connection_holder)

Add a connection holder that will be filled in before run.

Parameters:

pre_run_connection_holder (ConnectionHolder) – The connection holder to be added

property connector

The connector connected to the synapse.

Return type:

AbstractConnector

property delays

The total synaptic delays (if any).

Return type:

float or list(float) or ndarray(float) or None

finish_connection_holders()

Finish all the connection holders, and clear the list so that they are not generated again later.

may_generate_on_machine()

Do we describe a collection of synapses whose synaptic matrix may be generated on SpiNNaker instead of needing to be calculated in this process and uploaded? This depends on the connector, the definitions of the weights and delays, and the dynamics of the synapses.

Returns:

True if the synaptic matrix may be generated on machine (or may have already been so done)

Return type:

bool

may_use_direct_matrix()

Do the properties of the synaptic information allow it to use the direct matrix?

Return type:

bool

property n_post_neurons

The number of neurons in the post-population.

Return type:

int

property n_pre_neurons

The number of neurons in the pre-population.

Return type:

int

property post_population

The population hosting the synapse.

Return type:

Population or PopulationView

property postpop_is_view

Whether the post_population() is a view.

Return type:

bool

property pre_population

The population sending spikes to the synapse.

Return type:

Population or PopulationView

property pre_run_connection_holders

The list of connection holders to be filled in before run.

Return type:

list(ConnectionHolder)

property prepop_is_view

Whether the pre_population() is a view.

Return type:

bool

property receptor_type

A string representing the receptor type.

Return type:

str

property synapse_dynamics

The dynamic behaviour of the synapse.

Return type:

AbstractSynapseDynamics

property synapse_type

The type of the synapse.

Return type:

int

property synapse_type_from_dynamics

Whether the synapse type comes from the synapse dynamics.

Return type:

bool

property weights

The synaptic weights (if any).

Return type:

float or list(float) or ndarray(float) or None