From: Rüdiger Koch (rkoch@rkoch.org)
Date: Mon Apr 01 2002 - 14:43:12 MST
On Monday 01 April 2002 23:18, Adrian Tymes wrote:
> Rüdiger Koch wrote:
> > On Monday 01 April 2002 06:41, you wrote:
> >>An ANN consists of a series of nodes designed to mimic biological
> >>neurons, thus the name. Each node takes a series of inputs, multiplies
> >
> > Don't confuse ANN in their wide spread implementation with biological
> > neurons. In most ANN simulators don't model neurons at all. They are
> > connectionist models, such as neural networks are connectionist, but
> > there the similarity ends. There are more realistic models available.
>
> Great. So, I rediscovered what someone else has discovered. Again.
> -_-
Where is your code?
> >>This differs from real biological neurons in that there is no abstract
> >>meta-signal to distinguish good from bad in the living system. A weight
> >>(amount and other properties of the synapses) gets increased if it
> >>fires, and decreases (decays) if it does not for a while. (This decay
> >>rate depends on which properties of the synapse have been enhanced:
> >>while a given synapse may become less sensitive after not firing for a
> >>day, newly formed synapses do not go away entirely at nearly as high a
> >>rate.) Thus, biological neurons can learn by repeating a pattern of
> >>stimulus - but this requires something to determine whether the stimulus
> >>repeats. Where humans are involved, say with a parent or teacher of a
> >>young child, this repetition can be provided by a human, in the hopes
> >>that not only the stimulus, but also the concept of deliberate
> >>repetition, is acquired - "learning how to learn".
> >
> > This is called Hebbian learning and is available in all decent ANN
> > simulators. I am not aware of meta-signals. If you have literature to
> > this topic, please point me to it! Hebbian learning seems not to be
> > enough, however. Something is missing....
>
> Like, say, initial weights geared towards the problem (though probably
> not encoding it exactly) and an ability to form connections where none
> were before?
>
> >>Certain patterns, or at least tendencies towards them, are pre-wired at
> >>birth (the initial weights of the system), with help from evolution.
> >
> > .... Every brain (of a given species) is locally very different even
> > though it globally looks the same. It's not plausible how a genome with
> > maybe 100MB of data is able to describe the synapses of a human brain
> > which is way more than 1PetaByte of data. The genome is not a blue print!
> > It's rather a program, the execution of which builds a phenotype. My
> > hypothesis is that the initial micro anatomy of the brain is such that
> > self-organizing by Hebbian and maybe other types of local learning is
> > possible.
>
> Like I said. We're in violent agreement here.
>
> >>If this is correct...then, would the above work as an algorithm for
> >>emulating uploaded animals? (Including humans, once Moore's Law gets us
> >
> > Nope. But I would not worry about this part of uploading technology. Once
> > scanning technology is developed enough we can scan a mouse and
> > instantiate it into different neuron models until we find it eats virtual
> > cheese ;-)
>
> But if we don't have the models, we don't know what to scan for...not
> exactly, anyway.
-- Rüdiger Koch http://rkoch.org Mobile: +49-179-1101561
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