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When I read about cognitive models that use the Semantic Pointer Architecture (SPA), such as a model for general intelligence, I often see a component called the "clean-up memory"? What's the function of this component and how does it function vary from SPA model to SPA model?

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SPA is used (among other things) for combining (binding) and extracting (unbinding) knowledge representations for processing. This is a (purposely) lossy compression. In the "Learning Rule Generation for Induction" case, the clean-up memory is used to convert a general transformation that is being learned (lots of different transformations convolved together) into a transformation that is already known. So basically it's associating the in-progress transformation to a known transformation.

This is why the "clean-up memory" is also called the "associative memory" in other SPA models and in Nengo 2.0.

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