<p>see Lisman, J. E. and Otmakhova, N. A. (2001), Storage, recall, and novelty detection of sequences by the hippocampus: Elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine. Hippocampus, 11: 551–568. doi: 10.1002/hipo.1071: <a href="http://wwww.bio.brandeis.edu/lismanlab/pdf/socratic.pdf" rel="nofollow">http://wwww.bio.brandeis.edu/lismanlab/pdf/socratic.pdf</a> for one proposed mechanism.</p>

<p>in short, local learning rules can develop connections between items that occur sequentially.  if you have one network that pattern completes (i.e. identifies) the input, you can pass that identification to a second network which learns temporal associations between items.  </p>

CC BY-SA 3.0
2016-01-14T19:43:21.950Z
Can you improve your language a bit and go into a bit more detail?
jona
CC BY-SA 3.0
2016-01-15T00:34:04.490Z
what more detail would you like?
honi
CC BY-SA 3.0
2016-03-07T08:59:27.573Z
Thanks for the edit. This sounds very reasonable. Together with STDP this answers my (not very precise) question.
danijar
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2016-01-14T18:57:03.227Z
2016-01-14T18:57:03.227Z
honi
<p>Local learning rules like <a href="http://psych.colorado.edu/~oreilly/papers/OReilly96_generec_nc.pdf" rel="nofollow noreferrer">Contrastive Hebbian Learning</a>, XCAL, etc. are based on the idea of strengthening edges when the neurons they connect fire simultaneously. This causes frequent patterns in the input stream to be encoded in the layers.</p>

<p>I understand how this attracts similar input patterns into learned activation states. However, I fail to see how this memory can be used to predict the future input. How is it possible for neurons to predict future input streams under local learning rules?</p>

<p><a href="https://i.sstatic.net/vrnPG.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vrnPG.jpg" alt="Initial and final states in Hebbian Learning"></a></p>

<p>From: <a href="http://slideplayer.com/slide/272753/" rel="nofollow noreferrer">Introduction Hebbian learning (2005)</a></p>

CC BY-SA 3.0
2016-03-07T01:27:20.107Z
What do you mean by "future input"?  Do you mean: If you had a system whose input was a grid of pixels and the output was the number the pixels were representing, how does the network generalise from the training examples to the testing examples?
Seanny123
<cognitive-neuroscience><learning><theoretical-neuroscience><neural-network><perceptual-learning>
How can neuronal networks with local learning rules predict their future inputs?
danijar
# How can neuronal networks with local learning rules predict their future inputs?

(Asked by: username_0 on 2016-01-14)

Local learning rules like [Contrastive Hebbian Learning](http://psych.colorado.edu/~oreilly/papers/OReilly96_generec_nc.pdf), XCAL, etc. are based on the idea of strengthening edges when the neurons they connect fire simultaneously. This causes frequent patterns in the input stream to be encoded in the layers.

I understand how this attracts similar input patterns into learned activation states. However, I fail to see how this memory can be used to predict the future input. How is it possible for neurons to predict future input streams under local learning rules?

[![Initial and final states in Hebbian Learning](https://i.sstatic.net/vrnPG.jpg)](https://i.sstatic.net/vrnPG.jpg)

From: [Introduction Hebbian learning (2005)](http://slideplayer.com/slide/272753/)

**Tags:** cognitive-neuroscience, learning, theoretical-neuroscience, neural-network, perceptual-learning

> **Comments:**
> **username_1**: What do you mean by "future input"?  Do you mean: If you had a system whose input was a grid of pixels and the output was the number the pixels were representing, how does the network generalise from the training examples to the testing examples?  

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## 1 answers

### Answer by username_2 on 2016-01-14. Score: 1 | Accepted answer

see Lisman, J. E. and Otmakhova, N. A. (2001), Storage, recall, and novelty detection of sequences by the hippocampus: Elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine. Hippocampus, 11: 551–568. doi: 10.1002/hipo.1071: <http://wwww.bio.brandeis.edu/lismanlab/pdf/socratic.pdf> for one proposed mechanism.

in short, local learning rules can develop connections between items that occur sequentially. if you have one network that pattern completes (i.e. identifies) the input, you can pass that identification to a second network which learns temporal associations between items.

> **Comments:**
> **username_3**: Can you improve your language a bit and go into a bit more detail?  
> **username_2**: what more detail would you like?  
> **username_0**: Thanks for the edit. This sounds very reasonable. Together with STDP this answers my (not very precise) question.  

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