How are they computationally simple?
Everyone assumes a graph network of nodes (neurons) and links
(axons/dendrites).
ERRORS - Crossing links may affect each other.
Everyone ignores local and global biochemical concentrations.
ERRORS - Neurotransmitters and ions affect firing rates.
Everyone assumes that all neurons have the same internal algorithm . . . .
etc., etc.
What do you mean by computationally simple? Explain to me how *you*
construct a neural network that takes all of this into account.
----- Original Message -----
From: "J. Andrew Rogers" <[EMAIL PROTECTED]>
To: <[email protected]>
Sent: Sunday, June 01, 2008 3:22 PM
Subject: Re: [agi] Ideological Interactions Need to be Studied
On Jun 1, 2008, at 12:17 PM, Mark Waser wrote:
Neurons are *NOT* simple. There are all sorts of physiological features
that affect their behavior, etc. While I totally agree with your point
about "Not only do you have to invent several new layers of abstraction,
you also have to invent the control structures to manage all those
abstractions and layers." -- as far as I'm concerned, ASSERTing clearly
incorrect statements like "Neurons *are* simple" totally invalidates
your credibility.
Neurons are structurally complex but computationally simple within the
usual constraints of computational information theory. Only the latter
matters since (presumably) no one is attempting to build actual neurons
to get the job done.
J. Andrew Rogers
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