Note

This documentation is for a development version. Click here for the latest stable release (v1.3.0).

nengo_spa¶

nengo_spa.ActionSelection
nengo_spa.create_inhibit_node
nengo_spa.ifmax
nengo_spa.Network
nengo_spa.sym

class nengo_spa.ActionSelection[source]¶

Implements an action selection system with basal ganglia and thalamus.

The ActionSelection instance has to be used as context manager and each potential action is defined by an ifmax call providing an expression for the utility value and any number of effects (routing of information) to activate when this utility value is highest of all.

See also

nengo_spa.modules.BasalGanglia: Default basal ganglia network
nengo_spa.modules.Thalamus: Default thalamus network

Examples

with ActionSelection():
    ifmax(dot(state, sym.A), sym.B >> state)
    ifmax(dot(state, sym.B), sym.C >> state)
    ifmax(dot(state, sym.C), sym.A >> state)

This will route the B Semantic Pointer to state when state is more similar to A than any of the other Semantic Pointers. Similarly, C will be routed to state when state is B. Once, state is C, it will be reset to A and the cycle begins anew.

Further action selection examples:

Controlled question answering

Parsing simple commands

Routing through a sequence

Routed sequencing

Attributes

activeActionSelection: Class attribute providing the currently active ActionSelection instance (if any).
builtbool: Indicates whether the action selection system has been built successfully.
bgnengo.Network: Basal ganglia network. Available after the action selection system has been built.
thalamusnengo.Network: Thalamus network. Available after the action selection system has been built.

nengo_spa.create_inhibit_node(net, strength=2.0, **kwargs)[source]¶

Creates a node that inhibits all ensembles in a network.

Parameters

netnengo.Network: Network to inhibit.
strengthfloat: Strength of the inhibition.
**kwargsdict: Additional keyword arguments for the created connections from the node to the inhibited ensemble neurons.

Returns

nengo.Node: Node that can be connected to, to provide an inhibitory signal to the network.

nengo_spa.ifmax([name, ]condition, actions)¶

Defines a potential action within an ActionSelection context.

Parameters

name (str) – Name for the action. Can be omitted.
condition – The utility value for the given actions.
actions – The actions to activate if the given utility is the highest.

Returns

Nengo object that can be connected to, to provide additional input to the utility value.

class nengo_spa.Network(label=None, seed=None, add_to_container=None, vocabs=None)[source]¶

Base class for SPA networks or modules.

SPA modules are networks ethat declare their inputs and outputs with associated Vocabulary instances. These inputs and outputs can then be be used in the SPA syntax, for example module1.output >> module2.input. Inputs and outputs named default can be omitted in the SPA syntax so that one can write module1 >> module2.

Furthermore, SPA modules allow to configure parameters of contained SPA modules, for example:

with spa.Network() as net:
    net.config[spa.State].vocab = 32
    state = spa.State()  # Will now have a 32-dimensional vocabulary

Parameters

labelstr, optional: Name of the network.
seedint, optional: Random number seed for the network.
add_to_containerbool, optional: Determines if this network will be added to the current container.
vocabsVocabularyMap, optional: Maps from integer dimensionalities to the associated default vocabularies.

Attributes

vocabsVocabularyMap: Maps from integer dimensionalities to the associated default vocabularies.

property config¶: Configuration for this network.

classmethod get_input_vocab(obj)[source]¶: Get the vocabulary associated with an network input obj.

classmethod get_output_vocab(obj)[source]¶: Get the vocabulary associated with an network output obj.

declare_input(obj, vocab)[source]¶

Declares a network input.

Parameters

objnengo.base.NengoObject: Nengo object to use as an input to the network.
vocab: Vocabulary: Vocabulary to assign to the input.

declare_output(obj, vocab)[source]¶

Declares a network output.

Parameters

objnengo.base.NengoObject: Nengo object to use as an output of the network.
vocabVocabulary: Vocabulary to assign to the output.

nengo_spa.sym¶

Provides Semantic Pointer symbols for symbolic expressions that are not tied to a single vocabulary. The vocabulary will be determined from the context. To use a symbol access it as an attribute on this object.

For example the following:

sym.A * sym.B >> state

is equivalent to:

state.vocab.parse('A * B') >> state

Further members¶

Commonly used classes and functions are accessible at the top level of the nengo_spa package.

nengo_spa.vocabulary.Vocabulary

A collection of semantic pointers, each with their own text label.

SPA Modules¶

`nengo_spa.modules.AssociativeMemory`	General associative memory network.
`nengo_spa.modules.IAAssocMem`	Associative memory based on the `IA` network.
`nengo_spa.modules.ThresholdingAssocMem`	Associative memory based on `Thresholding`.
`nengo_spa.modules.WTAAssocMem`	Associative memory based on the `WTA` network.
`nengo_spa.modules.BasalGanglia`	Winner take all network, typically used for action selection.
`nengo_spa.modules.Bind`	Network for binding together two inputs.
`nengo_spa.modules.Compare`	Computes the dot product of two inputs.
`nengo_spa.modules.Product`	Multiplies two scalars.
`nengo_spa.modules.Scalar`	Represents a single scalar.
`nengo_spa.modules.State`	Represents a single vector, with optional memory.
`nengo_spa.modules.Thalamus`	Inhibits non-selected actions.
`nengo_spa.modules.Transcode`	Transcode from, to, and between Semantic Pointers.

Examination of Semantic Pointers¶

`nengo_spa.examine.pairs`	Return expressions for all possible combinations to bind vocab’s keys.
`nengo_spa.examine.similarity`	Return the similarity between simulation data and Semantic Pointers.
`nengo_spa.examine.text`	Return a human-readable text version of the provided vector.

Operators¶

`nengo_spa.operators.dot`	Dot-product between a and b.
`nengo_spa.operators.reinterpret`	Reinterpret source Semantic Pointer as part of vocabulary vocab.
`nengo_spa.operators.translate`	Translate source Semantic Pointer to vocabulary vocab.