Sergio,
Your words sound nice in theory, but that is not the way it is happening
on the ground.
What I tried to say was that neuroscience folks are far too quick to
deploy words like "cognition" and "concepts" and "consciousness"
(apparently as a way to sound impressive) when in fact their
justification for using those words is appalling. They often seem not
understand the way those words are used (by cognitive scientists), and
their attempts to link the words to their models or observations are,
quite frankly, a joke. (I am being blunt, but that is because there are
strong feelings on the cognitive science side of this situation.)
The point of my presenting that "analogy" quote was to say that THIS is
what it looks like to the cognitive scientist who reads what they put
out, so you are slipping the point sideways a little when you say that
maybe the influence of noise is more important in neurons than it would
be in transistors.
I was not lampooning it at that level, I was saying that a
neuroscientist who started off by claiming great progress in
fantastically high-level areas (cognition) is being idiotic when they
jump all the way down and suddenly begin to talk about *any* aspect of
the neuron level directly, without just cause. My analogy was a person
who claimed to be studying high-level software architecture of huge
systems, but who then spent most of their time talking about transistors
.... and then occasionally popped up to the top level and said (e.g.)
"this looks like the concept of "object persistence" in OOP systems!"
That person could not claim to be doing just "early science," trying to
establish what the right concepts might be, etc (the way you frame it),
because there already exists (using my analogy) an entire field of
people who know a great deal about terms like objects, object
persistence, message passing and so on. It ain't early stage science
unless that latter crowd is ignored, as if they did not exist!
Now that is the problem.
You can get a flavor of some aspects of what I am ranting about ;-) from
a special issue of Cognitive Neuropsychology, the lead article of which was:
Harley, T. A. (2004). Does cognitive neuropsychology have a future?
Cognitive Neuropsychology, 21, 3-16.
Trevor Harley and I are currently working on a paper in this area.
Richard Loosemore
Sergio Navega wrote:
Richard,
I'm not sure your substitutions of words constitute a valid
criticism of the computational neuroscience field. Sure, I may
concede that some of the categories they introduce may suffer
from some serious flaws. But we cannot forget that we still don't
know how the whole neural system works (as opposed to our very
good understanding of how a bunch of transistors give rise to
the US Air Traffic Control System). The way computational
neuroscientists introduce concepts (and often mix levels of
analyses) should be seen as the way scientific reasoning works
in fields with a lot to discover. The scientific method allows
the construction of such abstract notions, because it has
some chance of being appropriate and if they are not, the method
will eventually discard or substitute them for better notions.
History of science is filled with such things. In transistors,
stochastic behavior is not important. In neurons, it seems an
important factor. But what is the influence of noise in these
systems? What good does it do? We will only discover by
postulating theoretical constructions capable of generating
predictions and letting the evidences guillotine the bad models.
Sergio Navega.
----- Original Message ----- From: "Richard Loosemore" <[EMAIL PROTECTED]>
To: <[email protected]>
Sent: Wednesday, October 11, 2006 11:47 AM
Subject: Re: [agi] Fwd: Articles in this week's Science
[Warning. The following is short and brutally frank.]
This first article (which overviews the others) is a typical piece of
neuroscience BS - at least from the point of view of a substantial
number of people in the cognitive science community.
To get an idea why, let me quote to you three excerpts from the piece,
but translating them into claims about high level aspects of
COMPUTING, instead of high level aspects of COGNITION.
Rememember, these neuroscience folks are trying to sell the idea that
they are making progress towards understanding how the entire brain
works as a mind, not as a bunch of neurons. They don't claim to be
doing neurophysiology, they claim to be making a link to cognition,
thought, consciousness and the like. So, in my edited versions below,
I have changed references to high level cognition to become references
to large software systems. With this change, the essential
vacuousness of the claims sticks out like a sore thumb.
****************************************************
"Understanding the dynamics and computations of single [transistors]
and their role within [computer circuits] is at the center of
neuroscience. How do single-[transistor] properties contribute to
information processing and, ultimately, to [the behavior of extremely
complex software systems]? What level of description is required when
modeling single [transistors]? Herz et al. (p. 80) review
single-[transistor] models..........."
"Single [transistors] are part of larger networks. Destexhe and
Contreras (p. 85) review advances in the computations created by
stochastic input in [transistors] and networks of [transistors]. They
emphasize the importance of irregular activity in [transistor]
computations............"
"On a higher processing level, computational neuroscience based on [an
approximate circuit diagram (with only 1% of the wires transistors
actually showing)] of the [Intel Xeon] can help us understand the
complexities of [the highest-level aspects of the software that runs
the US Air Traffic Control System]. O'Reilly (p. 91) reviews
developments in models, of [the software that runs the US Air Traffic
Control System]. He develops the idea that the [floating point unit in
an Intel Xeon] represents a synthesis between analog and digital forms
of computation.........."
****************************************************
If someone said that there was an entire field that was making
progress in understanding the issues involved in the design and
functioning of the largest software/hardware projects on the planet,
and if the people in this field grabbed an issue of Science to present
shining examples of their best work, as above, what would you think?
The emperor has no clothes.
Richard Loosmore
Ben Goertzel wrote:
There's a special section in this week's Science called "Modeling the
Mind"
that should be of interest to many denizens of this list. Here are the
titles:
Of Bytes and Brains
Peter Stern and John Travis
Science 6 October 2006: 75.
<http://www.sciencemag.org/cgi/content/summary/314/5796/75> Summary >|
PDF <http://www.sciencemag.org/cgi/reprint/314/5796/75.pdf> >|
News
An Enterprising Approach to Brain Science
Greg Miller
Science 6 October 2006: 76-77.
<http://www.sciencemag.org/cgi/content/summary/314/5796/76> Summary >|
Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/76>
>| PDF
<http://www.sciencemag.org/cgi/reprint/314/5796/76.pdf> >|
Vision's Grand Theorist
Ingrid Wickelgren
Science 6 October 2006: 78-79.
<http://www.sciencemag.org/cgi/content/summary/314/5796/78> Summary >|
Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/78>
>| PDF
<http://www.sciencemag.org/cgi/reprint/314/5796/78.pdf> >|
Reviews
Modeling Single-Neuron Dynamics and Computations: A Balance of Detail
and
Abstraction
Andreas V. M. Herz, Tim Gollisch, Christian K. Machens, and Dieter
Jaeger
Science 6 October 2006: 80-85.
<http://www.sciencemag.org/cgi/content/abstract/314/5796/80> Abstract >|
Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/80>
>| PDF
<http://www.sciencemag.org/cgi/reprint/314/5796/80.pdf> >|
Neuronal Computations with Stochastic Network States
Alain Destexhe and Diego Contreras
Science 6 October 2006: 85-90.
<http://www.sciencemag.org/cgi/content/abstract/314/5796/85> Abstract >|
Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/85>
>| PDF
<http://www.sciencemag.org/cgi/reprint/314/5796/85.pdf> >|
Biologically Based Computational Models of High-Level Cognition
Randall C. O'Reilly
Science 6 October 2006: 91-94
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