nengo-spa 0.3.0

An implementation of the Semantic Pointer Architecture for Nengo

Nengo SPA: Implementation of the Semantic Pointer Architecture for Nengo

Project status

• All of the core functionality is implemented, but the API might still be subject to changes.

• The documentation needs extensive updates.

• The integration with the Nengo GUI is not implemented yet.

Installation

Nengo SPA depends on Nengo 2.4+, and we recommend that you install Nengo before installing Nengo SPA.

To install Nengo SPA:

pip install nengo_spa

Nengo SPA is tested to work on Python 2.7 and 3.4+.

Documentation

TODO

Getting Help

Questions relating to Nengo and Nengo SPA, whether it’s use or it’s development, should be asked on the Nengo forum at https://forum.nengo.ai.

Release History

0.3.0 (October 16, 2017)

Added

• Add add_output and add_neuron_output methods to IdentityEnsembleArray to provide the full API that is provided by the regular Nengo EnsembleArray. (#61, #28)

• Add create_inhibit_node function to create nodes that inhibit complete Nengo networks. (#65, #26)

• Add a solver argument to the action rule’s translate to use a solver instead of an outer product to obtain the transformation matrix which can give slightly better results. (#62, #57)

Changed

• Actions rules do not require module to be assigned to the model any longer. They will access exactly the same variables as are available in the surrounding Python code. This means that existing action rules need to be changed to reference the correct names. (#63)

• The action rule syntax changed significantly. (#54, #72)

• Actions will be build automatically without an explicit call to build(). (#59, #45, #55)

• Consolidated the functionality of Encode and Decode into Transcode. (#67, #58)

Fixed

• Fix some operations changing the dimensionality of semantic pointers with an odd initial dimensionality. (#52, #53)

• When building actions the basal ganglia and thalamus will only be created when actually required. (#60, #42)

• The vocabulary translate mechanism will properly ignore missing keys in the target vocabulary when populate=False. (#62, #56)

• Allow empty string as argument to Vocabulary.populate. (#73)

0.2 (June 22, 2017)

Added

• Tutorial explaining what has changed in nengo_spa compared to the legacy SPA implementation. (#46)

• Examples can be extracted with python -m nengo_spa extract-examples <destination>. (#49, #7)

Changed

• Replaced input_keys and output_keys arguments of associative memories with a single mapping argument. (#29, #8)

• Replaced ampa_config and gaba_config parameters of the BasalGanglia with ampa_synapse and gaba_synapse parameters. Removed the general_config parameter. (#30, #23)

Fixed

• Improved a number of error messages. (#35, #32, #34)

• Improved accuracy by fixing choice of evaluation point and intercept distributions. (#39)

• Correctly apply transforms on first vector in vocabularies on on non-strict vocabularies. (#43)

0.1.1 (May 19, 2017)

Fixed

• Updated the 0.1 changelog.

0.1 (May 19, 2017)

Initial release of Nengo SPA with core functionality, but excluding

• updates and completion the documentation,

• proper integration with Nengo GUI.

The API is still conisdered unstable in some parts of it are likely to change in the future.

Main features compared to the SPA implementation shipped with Nengo are:

• neural representations have been optimized for higher accuracy,

• support for arbitrarily complex action rules,

• SPA networks can be used as normal Nengo networks,

• and SPA networks can be nested.