On Nov 30, 2007 9:58 PM, Ed Porter <[EMAIL PROTECTED]> wrote:
> Vladimir Nesov>>>> There are no well-articulated theories here. I guess that
> columns are induction chips: they have potential all-to-all connectivity, so
> they can learn the rule in form 'after this signal comes that signal' for
> any two signals in column.
>
> Ed>>>> how does the induction chip avoid cross talk between "if ABCDEF then
> G" and "if ABCDEG then H"?
That's what I included "something not that
much more intricate than" mantra in my message for :)
In my current model there are context-sensitive links between nodes,
AND and NOT combinators. Whenever one node is active ('link origin'),
another node ('link target') will be activated, but only if
additionally yet another node is active/inactive (it's a
context-sensitivity part). Something like this is inevitable if only
to provide sensible repertoire of representable functions.
So to answer your example, let for simplicity assume that there's a
node X that is active whenever ABCDE is encountered, then XF->G can be
represented by a context-sensitive link from X to G sensitive to F, or
G=AND(X,F). Learning of these context-sensitive links is still kind of
inductive, since such link is learned when triplet of nodes shows
regularities similar to ones imposed by link.
I guess choice of context-specifying parameter can be allowed to be
more limited, so there's a place for assemblies here (but only in the
sense of 'similar nodes'). Advantage over model without potential
all-to-all connectivity is that problem with learning induction as in
Valiant's model no longer applies. He used intermediate nodes to
account for induction, allowing inductive relation between A and B to
be learned whenever there were neurons C _between_ A and B, so that
activation would follow A->C->B path. I can't see how C will _locally_
know to respond to A (above other things it hears from) if B listens.
When there's a all-to-all connectivity, this magical step is not
needed.
Additionally, and it's the reason I used delays in that
numeric-parameters discussion last month, it's nice to have the
ability to include delays in parameters of links. It allows temporal
patterns to be learned by the same rule. So, with XF->G, if "XF" is a
sequence where X is active at tact n, F at n+1 and G needs to be
activated at tact n+2, rule is G=AND(X:2,F:1), meaning that G will be
activated if X was activated 2 tacts ago and F was activated 1 tact
ago. It for example allows to use link G=NOT(X:2,G:1) instead of
G=AND(X:2,F:1) to distinguish between XF->G and XG->H (rule
G=NOT(X:2,G:1) means 'G will be activated whenever X was active 2
tacts ago, but G was not active 1 tact ago').
>
>
> -----Original Message-----
> From: Vladimir Nesov [mailto:[EMAIL PROTECTED]
> Sent: Wednesday, November 28, 2007 6:19 PM
> To: [email protected]
>
> Subject: Re: Cortical Columns [WAS Re: [agi] Funding AGI research]
>
> Edward,
>
> There are no well-articulated theories here. I guess that columns are
> induction chips: they have potential all-to-all connectivity, so they
> can learn the rule in form 'after this signal comes that signal' for
> any two signals in column. My current bet is that something not that
> much more intricate than this single rule is sufficient to implement
> cognition (and on von Neumann architecture you can implement one huge
> column of billion 'neurons' that does roughly the same). So I see
> columns as small AGI chips that operate within their narrow sensory
> input/output and communicate with each other to form overall behavior
> of the brain. Compartmentalization corresponds to character of
> concepts with which columns in particular area mostly deal.
>
> In this view 'neuronal assembly' corresponds to either collection of
> neurons (or dendrite fragments?) that represents very similar shades
> of given concept, or to different neurons in different columns that
> correspond to the same concept and implement inter-column interaction.
>
> For example, perception of an object can proceed starting from
> different senses (e.g. vision/hearing), and particular object can be
> detected locally and separately by subsystems involved in processing
> of each kind of these senses. In this case separate groups of columns
> work with information about the same object, but this information is
> only integrated on a higher stage of perception. Does it make these
> neurons that independently detect the same object/event, part of the
> same 'assembly'? It just might.
--
Vladimir Nesov mailto:[EMAIL PROTECTED]
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