[Comp-neuro] puzzlement over machine consciousness

Mario Negrello mnegrello at gmail.com
Wed Aug 13 21:47:36 CEST 2008


Hopping in the thread.

Confusion with analogies is, to my mind, what causes the confusions of  
machine consciousness. It is not 'flesh chauvinism' that argues  
against multiple realizability of consciousness. It is the fact that,  
if the building bricks are different (neurons ~= cpus), there will be  
different implications, different potentialities. A simulation may  
produce time series of membrane potentials that are virtually  
indistiguishable from the real thing. But the real thing has  
concomitants that go way beyond the time series (BTW, embodiement does  
not solve the problem).

Putnam, considered the founding father of mental functionalism (in a  
blurb, the idea that emulating the function of the brain suffices for  
mind), has since recant his views (Representation and Reality, 1988).  
Unfortunatelly, his reversal was not timely, as the idea is now  
irreparably installed.

Mathematically, it may well be that, from a certain level of analysis,  
the neuron acts like a circuit. But the components of the neuron and  
of an electronic circuit are fundamentally different, of course. And  
to the extent that a circuit is not a cell, not all potentialities of  
the cell are realized in a circuit, even if all 'computational  
properties' are. Emulating the biophysical processes is not playing  
the processes. Matter matters.

So, until we know what kind of processes are fundamental for the mind,  
it is safer to assume that all levels of the nervous system are  
necessary. There is no Gedankenexperiment that can dispute this. A  
simulation of a tornado is not a tornado (don't remember who said this).

Or is there?


On 13/ago/08, at 20:12, james bower wrote:

> just for clarity -- I was not comparing brains to airplanes --
>
> but software systems to engineer planes - to software systems to  
> reverse engineer the brain.
>
> As others have pointed out, humans since the dawn of written  
> language have always compared the mechanisms of human thinking  
> (brains) to whatever is the most sophisticated and powerful  
> technology of the day.
>
> Greek Roman: neural humors - aquifers
> Descartes: gears - machines
> early 20th century - telegraph systems
> middle 20th century - digital computers
> later 20th century - parallel distributed analog computing devices
>
> the problem comes when, instead of thinking of current technology as  
> perhaps the best, but often bad, metaphor,  we actually come to  
> believe that the brain IS one of those things.
>
> On functional and thermodynamic evidence alone, we have never come  
> close to building something as sophisticated or complex  or  
> efficient as the brain - therefore, we need to be very careful about  
> comparing the things we do build to brains.
>
> As a corollary, encouraged by funding agencies (see the post about  
> the recent meeting at NSF on the challenges related to understanding  
> the neural basis for learning) the claim is often made that we are  
> close to applying what we know about the brain to the construction  
> of real devices to solve real world (often military - they have more  
> money than anyone) problems.  The problem with those claims is that  
> the subject and approach usually more reflect the problems as  
> identified by the state of our technology ( in this case machine  
> learning), than anything much having to do with the brain -- again,  
> metaphor run amuck.
>
> In the particular case of the NSF meeting for example, from the  
> report I see little evidence that the question posed on this list  
> earlier - as to how much learning the brain actually performs, and  
> how much of the performance we attribute to learning is actually a  
> very sophisticated application of prior (evolutionary) knowledge  
> already built into the networks was even raised as an issue (seems  
> to me it is a critical issue).  To their credit, cognitive  
> neuroscientists and of course philosophers know about this problem  
> -- but most neural network engineers and neurobiologists don't  
> consider it.  Of course, if you are building a "learning machine" by  
> definition it mostly 'learns' -- and thus - the question of prior  
> knowledge is irrelevant, and thus out of place in this kind of  
> meeting.
>
> And therefore, again, the problem of the assumptions we make going  
> in influencing what comes out.
>
> Jim Bower
>
>
>
>
> On Aug 13, 2008, at 12:26 PM, Bill Lytton wrote:
>
>>
>>> as a physicist by education but working in neuroscience, I am  
>>> really puzzled about the
>>> approach to start from the single neuron to understand information  
>>> processing.
>>
>> I am puzzled by your puzzlement
>>
>> There is great complexity in a neuron that directly effects its I/O
>> -- the internal quantum complexity of a neutral particle doesn't  
>> much effect its billiard-ball
>> like behavior; the complexity of H2O does start to effect its  
>> interactions which makes aqueous
>> solutions difficult to model and to reduce cleanly to an analytic  
>> form
>>
>> the brain is often compared to a computer, and sometimes, as  
>> implicitly by Jim, to a modern
>> aircraft -- complexity arising from the connection of complex  
>> components.  Individual
>> neuron types differ in their complexity, perhaps lying somewhere  
>> between an op-amp (a granule
>> cell) and an 8086 CPU (2e4 transistors) -- a Purkinje cell
>>
>> there are papers by a phys-type that lays this out in a relatively  
>> formal way
>> Csete, ME and Doyle, JC, Science 295:1664-1669, 2002
>> Carlson, JM and Doyle, JC, PNAS    99:2538-2545, 2002
>> I recall that they also compare brains to airplanes
>>
>> Bill
>>
>> -- 
>> William W. Lytton, MD
>> Professor of Physiology, Pharmacology, Biomedical Engineering,  
>> Neurology
>> State University of NY, Downstate Medical Center, Brooklyn, NY
>> billl at neurosim.downstate.edu http://it.neurosim.downstate.edu/~billl
>> ________________________________________________________________
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>> Comp-neuro mailing list
>> Comp-neuro at neuroinf.org
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>
>
>
>
> ==================================
>
> Dr. James M. Bower Ph.D.
>
> Professor of Computational Neuroscience
>
> Research Imaging Center
> University of Texas Health Science Center -
> -  San Antonio
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