<p>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.</p>

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

CC BY-SA 3.0
2015-01-11T01:00:35.860Z
2015-01-11T01:00:35.860Z
Seanny123
<p>When I read about cognitive models that use the Semantic Pointer Architecture (SPA), such as <a href="http://compneuro.uwaterloo.ca/publications/rasmussen2011.html" rel="nofollow">a model for general intelligence</a>, 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?</p>

<cognitive-modeling><nef><spa>
What's the function of the clean-up memory in SPA?
Seanny123
# What's the function of the clean-up memory in SPA?

(Asked by: username_0 on 2015-01-10)

When I read about cognitive models that use the Semantic Pointer Architecture (SPA), such as [a model for general intelligence](http://compneuro.uwaterloo.ca/publications/rasmussen2011.html), 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?

**Tags:** cognitive-modeling, nef, spa


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## 1 answers

### Answer by username_0 on 2015-01-11. Score: 4 | Accepted answer

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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