Connectionists: How the brain works

[Brian J Mingus]

> Do you need to model quantum mechanics?  No (sorry Penrose) - One of the
straw men raised when talking about realistic models is always:   “at what
level do you stop, quantum mechanics?”.  The answer is really quite simple,
you need to model biological systems at the level that evolution
(selection) operates and not lower.  In some sense, what all biological
investigation is about, is how evolution has patterned matter.  Therefore,
if evolution doesn’t manipulate at a particular level, it is not necessary
 to understand how the machine works.

It seems that the efficiency of photosynthesis in leaves suggests that
natural selection has led to a biological system that exploits quantum
computation at least once.

Consciousness is also such a bag of worms that we can't rule out that
qualia owes its totally non-obvious and a priori unpredicted existence to
concepts derived from quantum mechanics, such as nested observers, or
entanglement.

As far as I know, my litmus test for a model is the only way to tell
whether low-level quantum effects are required: if the model, which has not
been exposed to a corpus containing consciousness philosophy, then goes on
to independently recreate consciousness philosophy, despite the fact that
it is composed of (for example) point neurons, then we can be sure that
low-level quantum mechanical details are not important.

Note, however, that such a model might still rely on nested observers or
entanglement. I'll let a quantum physicist chime in on that - although I
will note that according to news articles I've read that we keep managing
to entangle larger and larger objects - up to the size of molecules at this
time, IIRC.


Brian Mingus
http://grey.colorado.edu/mingus


On Mon, Jan 27, 2014 at 6:56 AM, james bower <bower at uthscsa.edu> wrote:

> A couple of points I have actually been asked (under the wire) to comment
> on:
>
> Do you need to model quantum mechanics?  No (sorry Penrose) - One of the
> straw men raised when talking about realistic models is always:   “at what
> level do you stop, quantum mechanics?”.  The answer is really quite simple,
> you need to model biological systems at the level that evolution
> (selection) operates and not lower.  In some sense, what all biological
> investigation is about, is how evolution has patterned matter.  Therefore,
> if evolution doesn’t manipulate at a particular level, it is not necessary
>  to understand how the machine works.
>
> Thomas’ original post regarding radios is actually illuminating on this
> point.  It starts by saying:  "I guess it uses an antenna to sense an
> electromagnetic wave that is then  amplified so that an electromagnet can
> drive a membrane to produce an airwave that can be sensed by our ear. Hope
> this captures some essential aspects.”  This level of description actually,
> captures the essential structure of a radio - i.e. the level of function of
> the radio at which the radio’s designers chose the components and their
> properties.  So, how proteins interact is important, the fact that that
> interaction depends on the behavior of electrons is not.  The behavior of
> electrons of course constrains how a particular molecule might interact -
> but evolution does not change the structure of electrons - (at least as far
> as I know).
>
> A other question that has been raised, somewhat defensively, has to do
> with the other level of modeling - and whether abstract models with no
> relationship to the actual structure of the nervous system, by definition,
> can not capture how the brain works?  there are several answers to this
> question.  The first and most obvious has to do with scientific process -
> how would you ever know?  If a model is not testable at the level of the
> machine and its parts - then there is no way to know and the model is not
> useful for what I am trying to do, which is to understand the machinery.
>  Put another way, if a model does not help in understanding “the
> engineering” (if you allow me the short hand), then it is also not useful
> in figuring out how the brain really works.
>
> There is another issue here as well - and that has to do with
> the likelihood that the model is correct.  This gets into issues of
> complexity theory, a subject many number of members of this list serve know
> better than I do.  However, I believe that one of the insights attributed
> to Kolmogorov  (who has been mentioned previously) is that there is a
> relationship between the complexity of a problem and its solution.  If
> there is a solution to a problem the brain solves that is simpler than the
> brain itself, then there is some constraint that has forced the brain to be
> more complex than it needs to be:  examples usually given include
> the component parts it has been ‘forced’ to work with, for example,
> or constraints imposed by the supposed sequential nature of evolutionary
> processes (making an arm from a fin).
>
> While it is at least worth considering whether the arm from fin argument
> applies to the nervous system, because we don’t understand how the brain
> works, we can’t really answer the question whether there is some simpler
> version that would have worked just as well.  Accordingly, as with the
> radio analogy, in principle, asking whether a simpler version would work as
> well, depends on first figuring it out how the actual system works.  As I
> have said, abstract models are less likely to be helpful there, because
> they don’t directly address the components.  However, while there is now
> finally some actual scientific work on this - still, most neurobiologists
> and even a lot of Neural network types, don’t seem to take into account how
> expensive brains are to run and the extreme pressure that has likely put on
> the brain to reach a ridiculous level of efficiency.  Accordingly, I am
> betting on the likelihood that the brain is not just some hacked solution,
> but in fact, may be an optimal solution to the problems it solves
> (remember, species also “pick” (again forgive the short hand) the problems
> they solve based on the structures they already have.  So, I myself assume
> that if there were a simpler physical solution, evolution would have found
> it.  At least, I can say with certainty that where ever it has been
> possible to measure the physical “ sophistication" of the nervous system,
> it is operating a very close to the constraints posed by physics (single
> photon detection by the eye, the ear operating just above the level of
> brownian noise, etc).  No reason, therefore not to assume until shown
> otherwise that the insides aren’t also “optimized”.  And, as I have said,
> we won’t be able to even address the question really, until we know how the
> thing works.
>
>
> That said, even if a simpler solution is as effective as the brain, I
> am interested in the brain.  Engineers are interested in building other
> devices and they would likely always prefer (for obvious reasons) to be as
> simple as possible. As I have said, I believe evolution is under the same
> constraint, actually, but in any event the question I have chosen to try to
> understand (perhaps foolishly) is how brains really work - not if something
> else could do the same thing using a simpler form.
>
> Finally, the above argument also relates to the issue of simple redundancy
>  - brain’s almost certainly can’t afford redundancy in the way that
> engineers have traditionally built in redundancy. To give credit where
> credit is due - one of the lessons that I did take from neural networks is
> that there are many more sophisticated ways to achieve fault tolerance than
> simple redundancy. Fault tolerance (this is how I actually think about
> learning, in fact) is a key requirement of brains. But almost certainly not
> accomplished by simple redundancy.
>
> Jim
>
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> On Jan 27, 2014, at 3:09 AM, Axel Hutt <axel.hutt at inria.fr> wrote:
>
> I fully agree with Thomas and appreciate, once more, the comparison with
> physics.
>
> Neuroscientists want to understand how the brain works, but maybe this is
> the wrong question. In physics,
> researchers do not ask this question, since, honestly, who knows how
> electrons REALLY move around
> the atom core ? Even if we know it, this knowledge is not necessary, since
> we have quantum theory that tells uns something about
> probabilities in certain states and this more abstract (almost mesoscopic)
> description is sufficient to
> explain large scale phenomena. Even on a smaller scale, quantum theory
> works fine, but only since physicists
> DO NOT ASK what electrons do in detail, but accept the concept. In
> contrary, todays neuroscience ask
> questions on all the details, what why ? Probably it is better to come up
> with a "concept" or more abstract
> model on neural coding, for sure on multiple description levels. But I
> guess, looking into too much much
> (neurophysiological) detail slows us down  and we need to ask other
> questions, maybe more directing
> towards experimental phenomena.
>
> A good example is the work of Hubel and Wiesel and the concept of columns
> (I know Jim and others do not like
> the concept), based on experimental data and deriving such a kind of
> concept. Of course these columns are not there
> 'physically' (there are no borders) but they represent more abstract
> functional units which allow to explain certain
> dynamical features on the level of neural populations (e.g. in the work on
> visual hallucinations). Today this concept
> is largely attacked since biologists 'do not see it'. But, again going
> back to physics, the trajetories of single electrons in
> atoms have not been measured yet and so the probability density of their
> location has not been computed yet from
> the single trajectories, but the resulting concept of probability orbits
> of electrons is well established today since it
> works well.
>
> Another analogy from physics (sorry to bore you, but I find the comparison
> important): do you believe that an
> object changes when you look at it (quantum theory says so) ? No, sure
> not, since you do not experience/measure it.
> But, hey, the underlying quantum theory is a good description of things.
> What I want to say: in neuroscience we
> need more theory based on physiological (multi-scale) experiments that
> describes the found data and permit to accept
> more (apparently) abstract models and get rid of our dogmatic view on how
> to do research. If an abstract description
> explains well several different phenomena, then per se it is a good
> concept (e.g. like the neural columns concept).
>
> Well, I have to go back to theoretical work, but it was very nice and
> stimulating attending this discussion.
>
> Axel
>
> ------------------------------
>
> Some of our discussion seems to be about 'How the brain works'. I am of
> course not smart enough to answer this question. So let me try another
> system.
> How does a radio work? I guess it uses an antenna to sense an
> electromagnetic wave that is then  amplified so that an electromagnet can
> drive a membrane to produce an airwave that can be sensed by our ear. Hope
> this captures some essential aspects.
> Now that you know, can you repair it when it doesn't work?
> I believe that there can be explanations on different levels, and I think
> they can be useful in different circumstances. Maybe my above explanation
> is good for generally curious people, but if you want to build a super good
> sounding radio, you need to know much more about electronics, even
> quantitatively. And of course, if you want to explain how the
> electromagnetic force comes about you might need to dig down into quantum
> theory. And to take my point into the other direction, even knowing all the
> electronic components in a computer does not tell you how a word processor
> works.
> A multilayer perception is not the brain, but it captures some interesting
> insight into how mappings between different representations can be learned
> from examples. Is this how the brain works? It clearly does not explain
> everything, and I am not even sure if it really captures much if at all of
> the brain. But if we want to create smarter drugs than we have to know how
> ion channels and cell metabolism works. And if we want to help stroke
> patients, we have to understand how the brain can be reorganized. We need
> to work on several levels.
> Terry Sejnowski told us that the new Obama initiative is like the moon
> project. When this program was initiated we had no idea how to accomplish
> this, but dreams (and money) can be very motivating.
> This is a nice point, but I don't understand what a connection plan would
> give us. I think without knowing precisely where and how strong connections
> are made, and how each connection would influence a postsynaptic or glia
> etc cells, such information is useless. So why not having the goal of
> finding a cure for epilepsy?
> I do strongly believe we need theory in neuroscience. Only being
> descriptive is not enough. BTW, theoretical physics is physics. Physics
> would not be at the level where it is without theory. And of course, theory
> is meaningless without experiments. I think our point on this list is that
> theory must find its way into mainstream neuroscience, much more than it
> currently is.  I have the feeling that we are digging our own grave by
> infighting and some narrow 'I know it all' mentality. Just try to publish
> something which is not mainstream even so it has solid experimental backing.
> Cheers, Thomas
>
>
>
>
> --
>
> Dr. rer. nat. Axel Hutt, HDR
> INRIA CR Nancy - Grand Est
> Equipe NEUROSYS (Head)
> 615, rue du Jardin Botanique
> 54603 Villers-les-Nancy Cedex
> France
> http://www.loria.fr/~huttaxel
>
>
>
>
>
>
> Dr. James M. Bower Ph.D.
>
> Professor of Computational Neurobiology
>
> Barshop Institute for Longevity and Aging Studies.
>
> 15355 Lambda Drive
>
> University of Texas Health Science Center
>
> San Antonio, Texas  78245
>
>
>
> *Phone:  210 382 0553 <210%20382%200553>*
>
> Email: bower at uthscsa.edu
>
> Web: http://www.bower-lab.org
>
> twitter: superid101
>
> linkedin: Jim Bower
>
>
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