<p>Deep Learning (which is now supposed to be called <a href="https://www.facebook.com/yann.lecun/posts/10155003011462143" rel="nofollow noreferrer">Differentiable Programming</a>?) is a means of doing inference over a set of data.</p>

<p>SPA is a Vector Symbolic Architecture (VSA) implemented using Holographic Reduced Representations (HRR). That is to say, sparse vectors represent symbols and there is a set of operations that can be used to combine them.</p>

<p>Consequently, SPA can be used as a sort of prior for Deep Learning to provide constraints on the representation it's learning.</p>

<p>For example, SPA could be used to represent the output of a visual system, which would then be used by other symbolic systems. In the case of Spaun, Deep Learning is used in the vision system to map images onto known symbols. These known symbols can then be manipulated in ways that would be impossible for Deep Learning to perform. For example, <a href="https://www.ncbi.nlm.nih.gov/pubmed/19431268" rel="nofollow noreferrer">Rapid Variable Creation</a> used for solving <a href="http://compneuro.uwaterloo.ca/publications/rasmussen2011.html" rel="nofollow noreferrer">Raven's Progressive Matrices</a> is impossible for Deep Learning to perform without any structural priors.</p>

CC BY-SA 3.0
2018-04-05T17:51:46.300Z
2018-04-05T17:51:46.300Z
Seanny123
<p>I have just started reading about the SPA (semantic pointer architecture) and wondering how it relates to deep learning concepts.</p>

CC BY-SA 3.0
2018-04-05T10:03:37.797Z
There is not a lot in your question to go on in order to answer your question. What do you know about SPA? What do you know about deep learning concepts? What is it you have read? What (if anything) is confusing you or hard to understand? If you have only just started to read about SPA and there is nothing confusing or hard to understand, maybe you just need to read more to fully understand it in order to answer your own question.
Chris Rogers
CC BY-SA 3.0
2018-04-05T17:39:00.313Z
Sorry @Chris, I'm the one who directed them to this site. I have should have given them more direction. erdal.karaca it would be helpful if you elaborated on your current understanding of each concept.
Seanny123
<spa>
How does SPA relate to deep learning concepts?
erdal.karaca
# How does SPA relate to deep learning concepts?

(Asked by: username_0 on 2018-04-04)

I have just started reading about the SPA (semantic pointer architecture) and wondering how it relates to deep learning concepts.

**Tags:** spa

> **Comments:**
> **username_1**: There is not a lot in your question to go on in order to answer your question. What do you know about SPA? What do you know about deep learning concepts? What is it you have read? What (if anything) is confusing you or hard to understand? If you have only just started to read about SPA and there is nothing confusing or hard to understand, maybe you just need to read more to fully understand it in order to answer your own question.  
> **username_2**: Sorry @Chris, I'm the one who directed them to this site. I have should have given them more direction. username_0 it would be helpful if you elaborated on your current understanding of each concept.  

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## 1 answers

### Answer by username_2 on 2018-04-05. Score: 2 | Accepted answer

Deep Learning (which is now supposed to be called [Differentiable Programming](https://www.facebook.com/yann.lecun/posts/10155003011462143)?) is a means of doing inference over a set of data.

SPA is a Vector Symbolic Architecture (VSA) implemented using Holographic Reduced Representations (HRR). That is to say, sparse vectors represent symbols and there is a set of operations that can be used to combine them.

Consequently, SPA can be used as a sort of prior for Deep Learning to provide constraints on the representation it's learning.

For example, SPA could be used to represent the output of a visual system, which would then be used by other symbolic systems. In the case of Spaun, Deep Learning is used in the vision system to map images onto known symbols. These known symbols can then be manipulated in ways that would be impossible for Deep Learning to perform. For example, [Rapid Variable Creation](https://www.ncbi.nlm.nih.gov/pubmed/19431268) used for solving [Raven's Progressive Matrices](http://compneuro.uwaterloo.ca/publications/rasmussen2011.html) is impossible for Deep Learning to perform without any structural priors.


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