Well, LTP is definitely real ... and I'm quite sure the scheme you
describe is *not* how learning works in the brain ;-) ,,, but I'm
equally sure that the full story has not yet been uncovered...

ben

On Wed, Dec 3, 2008 at 5:25 PM, Matt Mahoney <[EMAIL PROTECTED]> wrote:
>> http://www.newscientist.com/article/mg20026845.000-memories-may-be-stored-on-your-dna.html
>
> Actually, this makes sense. It explains most of the discrepancy between the 
> 10^9 bits of human long term memory estimated by Landauer and the 10^15 
> synapses in the human brain. If memory is stored in neurons (by gene 
> regulation to control activation threshold), then you have only 10^11 bits of 
> storage, or 1 bit per neuron.
>
> Here is how it could work. Imagine a neural network with fixed, randomly 
> weighted synapses. Then insert a neuron at each synapse with one input and 
> one output. Then you could apply Hebbian learning by modifying the 
> conductivity of the middle neuron. If the input and output neurons fire at 
> the same time, then the middle neuron would lower its threshold if both 
> weights are the same, or raise it if the weights have opposite sign. In other 
> words, instead of
>
> A -> B
>
> with a variable weight, you have
>
> A -> M -> B
>
> with a middle neuron M of variable conductivity and two fixed weights.
>
> Of course real neurons have thousands of inputs and outputs. This means that 
> there are thousands of neurons between A and B, and these middle neurons 
> connect to thousands of others. If these connections are random, then Hebbian 
> learning applied to these thousands of middle neurons would correlate only 
> with AB and create minor noise for other neurons.
>
> -- Matt Mahoney, [EMAIL PROTECTED]
>
>
>
> -------------------------------------------
> agi
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-- 
Ben Goertzel, PhD
CEO, Novamente LLC and Biomind LLC
Director of Research, SIAI
[EMAIL PROTECTED]

"I intend to live forever, or die trying."
-- Groucho Marx


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agi
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