learning functions more like STDP about comportex HOT 5 OPEN

@floybix Could this be related to pre-synaptic inhibition. Reading Spratling an co.'s work on "Pre-integration lateral inhibition enhances unsupervised learning", for example. Or Fergal's pre-pooler feedback twist?

http://www.inf.kcl.ac.uk/staff/mike/publications.html

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@rcrowder Fascinating reading, thanks (will take me a while to absorb it). The author claims is it just as biologically plausible as the usual post-integration lateral inhibition. But I would like to know what neuroscience experts think of it (and now, a decade after publication). Surely there is evidence on such a fundamental mechanism... Anyway, even if it is not biologically accurate, it may turn out to be computationally useful. I can't see how yet.

Not sure what you mean by "Fergal's pre-pooler feedback twist". Is that like a reverse somersault twist from pike position? :) If you mean "prediction-assisted CLA", i.e. biasing column activation towards those with predicted cells, that does not seem to help with the problem I described (sequence learning in a temporal-pooling layer).

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Hi Guys!

Felix, I'm curious, what is the state of the art with the "sequence
learning in a temporal-pooling layer" you are currently wrestling with?

Cheers,
David

On Fri, Aug 21, 2015 at 9:27 PM, Felix Andrews [email protected]
wrote:

@rcrowder https://github.com/rcrowder Fascinating reading, thanks (will
take me a while to absorb it). The author claims is it just as biologically
plausible as the usual post-integration lateral inhibition. But I would
like to know what neuroscience experts think of it (and now, a decade after
publication). Surely there is evidence on such a fundamental mechanism...
Anyway, even if it is not biologically accurate, it may turn out to be
computationally useful. I can't see how yet.

Not sure what you mean by "Fergal's pre-pooler feedback twist". Is that
like a reverse somersault twist from pike position? :) If you mean
"prediction-assisted CLA", i.e. biasing column activation towards those
with predicted cells, that does not seem to help with the problem I
described (sequence learning in a temporal-pooling layer).

—
Reply to this email directly or view it on GitHub
#25 (comment)
.

With kind regards,

David Ray
Java Solutions Architect

Cortical.io http://cortical.io/
Sponsor of: HTM.java https://github.com/numenta/htm.java

[email protected]
http://cortical.io

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@cogmission to be honest I don't know. Numenta people are doing various things probably including this, but as far as I know they don't have a working solution yet.

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If we take STDP seriously then the potentiation and depression effects should be symmetric:

(While we are not dealing with individual spikes, HTM cell activation presumably represents some aggregated function of spikes.)

A problem with current LTP-only learning approach is that cells can learn/grow connections to uninformative signals: if a source cell is constantly on, it will be learned. This may be part of why it is so hard to tune the influence of different senses. Every bit/cell that is on is treated equally.

But really I am not sure. Maybe we should learn connections contingent on constant signals just in case the whole regime/context changes later.

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