<p>An Associative Memory <em>is</em> a classifier and <em>is</em> equivalent to a Hamming Network.</p>

<p>For documentation on the NEF Associative Memory, see <a href="https://pythonhosted.org/nengo/examples/associative_memory.html" rel="nofollow">this practical Nengo documentation</a> and the paper "<a href="http://compneuro.uwaterloo.ca/files/publications/stewart.2011.pdf" rel="nofollow">A biologically realistic cleanup memory: Autoassociation in spiking neurons</a>". Basically, each ensemble of an associative memory computes the similarity measure of the input firing pattern to a given firing pattern. Mutual inhibition is then used between the nodes to find the most similar input so that only the ensemble with the most similar input outputs a signal. In an associative, this output similarity signal is used to map the input pattern to a new output pattern.</p>

<p><a href="http://home.agh.edu.pl/~vlsi/AI/hamming_en/" rel="nofollow">A Hamming Network</a> is a classifier that receives patterns with input nodes also detecting similarity. However, unlike the Associative Memory it has a binary output using mutual inhibition. That being said, it would be easy to add a thresholding ensemble to the output of the Associative Memory to accomplish this.</p>

<p>In conclusion, an Associative Memory is basically a Hamming Network implemented using the NEF.</p>

CC BY-SA 3.0
2016-08-12T15:27:15.270Z
2016-08-12T15:27:15.270Z
Seanny123
<p>After doing a bit of research, I'm somewhat convinced that Hamming networks are networks that classify, but only put out a single result, as opposed to other networks which output multiple results. In this case, is the equivalent in terms of the NEF a classifier (measuring similarity with a dot product between two vectors) followed by an associative memory?</p>

CC BY-SA 3.0
2015-04-08T08:05:18.803Z
Nice question, I'll have a look when I'm back home on Sunday.
Christian Hummeluhr
<neural-network><nef>
What is the equivalent of a Hamming network in the NEF?
Seanny123
# What is the equivalent of a Hamming network in the NEF?

(Asked by: username_0 on 2015-04-07)

After doing a bit of research, I'm somewhat convinced that Hamming networks are networks that classify, but only put out a single result, as opposed to other networks which output multiple results. In this case, is the equivalent in terms of the NEF a classifier (measuring similarity with a dot product between two vectors) followed by an associative memory?

**Tags:** neural-network, nef

> **Comments:**
> **username_1**: Nice question, I'll have a look when I'm back home on Sunday.  

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## 1 answers

### Answer by username_0 on 2016-08-12. Score: 2 | Accepted answer

An Associative Memory *is* a classifier and *is* equivalent to a Hamming Network.

For documentation on the NEF Associative Memory, see [this practical Nengo documentation](https://pythonhosted.org/nengo/examples/associative_memory.html) and the paper "[A biologically realistic cleanup memory: Autoassociation in spiking neurons](http://compneuro.uwaterloo.ca/files/publications/stewart.2011.pdf)". Basically, each ensemble of an associative memory computes the similarity measure of the input firing pattern to a given firing pattern. Mutual inhibition is then used between the nodes to find the most similar input so that only the ensemble with the most similar input outputs a signal. In an associative, this output similarity signal is used to map the input pattern to a new output pattern.

[A Hamming Network](http://home.agh.edu.pl/~vlsi/AI/hamming_en/) is a classifier that receives patterns with input nodes also detecting similarity. However, unlike the Associative Memory it has a binary output using mutual inhibition. That being said, it would be easy to add a thresholding ensemble to the output of the Associative Memory to accomplish this.

In conclusion, an Associative Memory is basically a Hamming Network implemented using the NEF.


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