In my opinion, biologically detailed large-scale
models of the brain offer little value if the
system is not embedded in a physical world. It
is a first step in the right direction to examine
vision. The brain is an adaptive system which
becomes useless if it is cut off the environment.
This brain-scale simulation of the neocortex
on the IBM Blue Gene/L supercomputer for
instance has brought little new insight.

Maybe it is useful to understand thalamocortical
oscillations, or to understand how neural assemblies
interact, but if you want to go beyond traditional
AI, maybe it is not recommendable to build a
biologically detailed large-scale model. A human
brain has not only more than 100 billion neurons
but also 100 trillion synapses. It is impossible
to model this in the finest level of detail.

I believe it is possible to achieve human-like
cognitive performance and self-consciousness
with computers, though, in the way I tried to
describe in the first post: if the processing is
parallel enough, if the model is not too biological,
and if the system embedded in some kind of physical
world (whether real or virtual). Maybe also a
RoadRunner which controls an agent in the
successor of SecondLife. Who knows..

Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer
http://www.research.ibm.com/journal/rd/521/djurfeldt.html

-J.
----- Original Message ----- 
From: "Marcus G. Daniels" <[EMAIL PROTECTED]>
To: "The Friday Morning Applied Complexity Coffee Group" <friam@redfish.com>
Sent: Sunday, July 20, 2008 10:48 PM
Subject: Re: [FRIAM] REPOST: The meaning of "inner".


>
> One code for investigating synthetic cognition is called PetaVision.
> This code was adapted to Roadrunner and, like LINPACK, exceeded 1000
> trillion floating point operations a second in recent benchmarks.
> Another project is the Blue Brain project at EPFL.
>


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