From: Brad Wyble <[EMAIL PROTECTED]>

>The jury is very much out Phillip.  Eliezer goes too far in saying it's a 
>myth perpetuated by computer scientists.  They use the simplest 
>representations they know to exist in their models for purposes of 
>parsimony.  It's hard to fault them for being rigorous in this respect.

Uploading is not the same as using ANNs to emulate the brain,
it has more to do with computational neuroscience and realistic
modeling.

>But neurons are surely far more complex than this.  The majority of 
>computation may well occur within the nonlinear bursting dynamics of 
>dendrites.

Nonlinear dendritic integration can be accurately captured by the
comparmental model which divides dendrites into small sections
with ion channels and other internal reaction mechanisms. This
is the most accurate level of modeling. It may be possible to
simplify this model with machine learning techniques and without
significant loss in accuracy.

>Just as an example, a new type of neuron has recently been discovered that 
>can hold a steady state of firing in isolation, apply current, rate 
>increases and remains stable at a new threshold.  It's dynamically 
>settable, which blows away all standard Integrate & Fire models.  

I don't know the exact mechanisms that give rise to that type
of neurons, but the comparmental model should be able to cover
this. What is needed is a large-scale database of neuronal
characteristics (automation).

>We're just scratching the surface of an enormous iceberg.  If you're
>trying to build some useful index of brain power based on number of
>neurons (or even synapses), give up and wait 30 years at least.

Intra-cellular signal processing is the most problematic
area, which requires proteomics to fully understand. I
don't know...

YKY


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