Steve Richfield wrote:
Richard,
On 11/20/08, *Richard Loosemore* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
Steve Richfield wrote:
Richard,
Broad agreement, with one comment from the end of your posting...
On 11/20/08, *Richard Loosemore* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]> <mailto:[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>>> wrote:
Another, closely related thing that they do is talk about low
level
issues witout realizing just how disconnected those are from
where
the real story (probably) lies. Thus, Mohdra emphasizes the
importance of "spike timing" as opposed to average firing rate.
There are plenty of experiments that show that consecutive
closely-spaced pulses result when something goes "off scale",
probably the equivalent to computing Bayesian probabilities >
100%, somewhat akin to the "overflow" light on early analog
computers. These closely-spaced pulses have a MUCH larger
post-synaptic effect than the same number of regularly spaced
pulses. However, as far as I know, this only occurs during
anomalous situations - maybe when something really new happens,
that might trigger learning?
IMHO, it is simply not possible to play this game without
having a close friend with years of experience poking mammalian
neurons. This stuff is simply NOT in the literature.
He may well be right that the pattern or the timing is more
important, but IMO he is doing the equivalent of saying
"Let's talk
about the best way to design an algorithm to control an airport.
First problem to solve: should we use Emitter-Coupled Logic
in the
transistors that are in oour computers that will be running the
algorithms."
Still, even with my above comments, you conclusion is still
correct.
The main problem is that if you interpret spike timing to be playing
the role that you (and they) imply above, then you are commiting
yourself to a whole raft of assumptions about how knowledge is
generally represented and processed. However, there are *huge*
problems with that set of implicit assumptions .... not to put too
fine a point on it, those implicit assumptions are equivalent to the
worst, most backward kind of cognitive theory imaginable. A theory
that is 30 or 40 years out of date.
OK, so how else do you explain that in fairly well understood situations
like stretch receptors, that the rate indicates the stretch UNLESS you
exceed the mechanical limit of the associated joint, whereupon you start
getting pulse doublets, triplets, etc. Further, these pulse groups have
a HUGE effect on post synaptic neurons. What does your cognitive science
tell you about THAT?
See my parallel reply to Ben's point: I was talking about the fact that
neuroscientists make these claims about high level cognition; I was not
referring to the cases where they try to explain low-level, sensory and
motor periphery functions like stretch receptor neurons.
So, to clarify: yes, it is perfectly true that the very low level
perceptual and motor systems use simple coding techniques. We have
known for decades (since Hubel and Weisel) that retinal ganglion cells
use simple coding schemes, etc etc.
But the issue I was discussing was about the times when neuroscientists
make statements about high level concepts and the processing of those
concepts. Many decades ago people suggested that perhaps these concepts
were represented by single neurons, but that idea was shot down very
quickly, and over the years we have found such sophisticated information
processing effects occurring in cognition that it is very difficult to
see how single neurons (or multiple redundant sets of neurons) could
carry out those functions.
This idea is so discredited that it is hard to find references on the
subject: it has been accepted for so long that it is common knowledge
in the cognitive science community.
The gung-ho neuroscientists seem blissfully unaware of this fact
because they do not know enough cognitive science.
I stated a Ben's List challenge a while back that you apparently missed,
so here it is again.
*You can ONLY learn how a system works by observation, to the extent
that its operation is imperfect. Where it is perfect, it represents a
solution to the environment in which it operates, and as such, could be
built in countless different ways so long as it operates perfectly.
Hence, computational delays, etc., are fair game, but observed cognition
and behavior are NOT except to the extent that perfect cognition and
behavior can be described, whereupon the difference between observed and
theoretical contains the information about construction.*
**
*A perfect example of this is superstitious learning, which on its
surface appears to be an imperfection. However, we must use incomplete
data to make imperfect predictions if we are to ever interact with our
environment, so superstitious learning is theoretically unavoidable.
Trying to compute what is "perfect" for superstitious learning is a
pretty challenging task, as it involves factors like the regularity of
disastrous events throughout evolution, etc.*
If anyone has successfully done this, I would be very interested. This
is because of my interest in central metabolic control issues, wherein
superstitious "red tagging" appears to be central to SO many age-related
conditions. Now, I am blindly assuming perfection in neural computation
and proceeding on that assumption. However, if I could recognize and
understand any imperfections (none are known), I might be able to save
(another) life or two along the way with that knowledge.
Anyway, this suggests that much of cognitive "science", which has NOT
computed this difference but rather is running with the "raw data" of
observation, is rather questionable at best. For reasons such as this, I
(perhaps prematurely and/or improperly) dismissed cognitive science
rather early on. Was I in error to do so?
I cannot make much detailed sense of what you say in the above (it seems
pitched at such a high level of abstraction and generality that I doubt
its validity). You speak of "perfection" and "imperfection" in ways
that make me confused about what sorts of perfection and imperfection
you could possibly mean.
However, having said all that, I can comment on your last paragraph.
You say that cognitive science is "running on raw data". I cannot find
any way to understand this statement that does not lead directly to the
conclusion that it is completely and utterly wrong. Cognitive science
involves a huge theoretical interpretation of raw data. You seem to be
implying that cognitive science is all about cataloguing data (or
something: I am really not sure what you mean). This is so far from
the truth that I can only express extreme astonishment that you would
say that.
Richard Loosemore
-------------------------------------------
agi
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