--- Richard Loosemore <[EMAIL PROTECTED]> wrote:

> Matt Mahoney wrote:
> > --- "John G. Rose" <[EMAIL PROTECTED]> wrote:
> >> Is there really a bit per synapse? Is representing a synapse with a bit
> an
> >> accurate enough simulation? One synapse is a very complicated system.
> > 
> > A typical neural network simulation uses several bits per synapse.  A
> Hopfield
> > net implementation of an associative memory stores 0.15 bits per synapse. 
> But
> > cognitive models suggest the human brain stores .000001 bits per synapse. 
> > (There are 10^15 synapses but human long term memory capacity is 10^9
> bits).
> 
> Sorry, I don't buy this at all.  This makes profound assumptions about 
> how information is stored in memory, averagng out the "net" storage and 
> ignoring the immediate storage capacity.  A typical synapse actually 
> stores a great deal more than a fraction of a bit, as far as we can 
> tell, but this information is stored in such a way that the system as a 
> whole can actually use the information in a meaningful way.
> 
> In that context, quoting "0.000001 bits per synapse" is a completely 
> meaningless statement.

I was referring to Landauer's estimate of long term memory learning rate of
about 2 bits per second.  http://www.merkle.com/humanMemory.html
This does not include procedural memory, things like visual perception and
knowing how to walk.  So 10^-6 bits is low.  But how do we measure such
things?

> Also, "typical" neural network simulations use more than a few bits as 
> well.  When I did a number of backprop NN studies in the early 90s, my 
> networks had to use floating point numbers because the behavior of the 
> net deteriorated badly if the numerical precision was reduced.  This was 
> especially important on long training runs or large datasets.

That's what I meant by "few".  In the PAQ8 compressors I have to use at least
16 bits.


-- Matt Mahoney, [EMAIL PROTECTED]

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singularity
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