Patrick Juola wrote in message <7hpk1p$u4$1 at quine.mathcs.duq.edu>...
>In article <3740344e at news3.us.ibm.net>, Sergio Navega <snavega at ibm.net>
wrote:
>>Jiri Donat wrote in message <7hoift$1ie$1 at nnrp1.deja.com>...
>>>>>>To me, the biggest difference between natural NN and ANN is that every
>>>digital simulation of ANN network has a discrete set of states (however
>>>large the set is). This "limitation" (if we understand this feature of
>>>digital representations of ANNs on today's computers as a limitation -
>>>and some theories do) is inherited in our existing tools for ANN
>>>simulations - in digital computers.
>>>>>>>>>I'm not sure I understand you here. In fact, biological neurons
>>seen from "outside" are just things that fire or don't fire, in a
>>purely discrete manner.
>>Seen from *FAR* outside, yes. In a similar fashion, from sufficiently
>far outside, the Solar System is a point mass.
>
My point exactly. If we're studying the kinematic behavior of the
milky way, it doesn't pay off to know the mass of Mars.
>> There don't seem to be any other meaningful
>>characteristic (such as waveshape or voltage) from the output of a
>>biological neuron, just the presence or not of the pulse.
>>This is incorrect. What's usually regarded as more important than
>the presence or absence of a pulse is the firing *rate*, measured
>as a scalar quantity. Different neurons respond at different rates
>depending on the circumstances, &c.
>
Although this is an open issue (not all neuroscientists agree with
firing rate), your assertion does not invalidate what I've said.
In other words, I said that each element of a spike train does
not seem to be differentiable by such characteristics as waveshape or
voltage, but only by the discrete presence or absence of the pulse
(while composing a scalar, mean firing rate or by means of timing
among each spike is something I didn't mention).
>Viewed in this light, the (scalar) activation level present at
>the output of an ANN unit is a model of the scalar activation level
>of the output of a real neuron. In more sophisticated models, this
>time course can be explicitly taken into account -- Birkbeck College,
>Univ. of London and Cal-tech both have active research groups looking
>at this sort of model.
>
I agree with that, but this is part of the open issue. Apparently
what is being settled is that neurons close to sensory inputs seem
to work considering the mean firing rate and that neurons of more inner
portions of the cortex care more for the "individual spikes". For
instance, it has been demonstrated by Steveninck and Bialek that
single spikes of the visual system of the fly contain significant
information about the stimulus.
But I think there's an additional contestant here, and this is
related to the proposals that put a role on the synchrony of
populations of neurons (Wolf Singer is an important name in this
regard). So although all these results are not meant to dismiss
current models of ANNs, I would certainly assume that what ANNs
model is a very different kind of thing than biological neurons.
Regards,
Sergio Navega.