[Comp-neuro] The sniffing brain - and free will

james bower bower at uthscsa.edu
Fri Aug 15 18:33:59 CEST 2008


Glad you came out in the open  :-)

> The question here is related to the first one: Is there a way in  
> computational neuroscience to verify any of these theories of  
> learning?

This, of course, is exactly the 'top down' kind of question that  
continues to worry me.  Of course, there is an enormous amount of work  
done in learning mechanisms in neurobiology.  It is a huge literature  
at all levels of scale.  There are also abundant examples of the  
influence of top down theories on brain science -- the one I  
personally have to deal with being the Marr/Albus theory of cerebellar  
learning -- which has produced an entire industry of neurobiologists  
intent on proving the idea to be correct -- even in the face of  
abundant evidence (even their own) to the contrary).

However, I have been deeply concerned for a long time that our  
emphasis on the importance of learning really derives from our belief  
(hope) in free will.  The Stealth Duelism of many cognitive brain  
models, I suspect, is similarly related to the deep held conviction  
that humans with our big brains, somehow operate as free entities,  
organizing our cognitive structures based on unique solutions to our  
own pattern of inputs.  See the posting today on variations in  
cognitive structures.  This, of course, has been a particularly  
prevalent view in the largely protestant United States.  (Ah, how I  
wish the Portuguese rather than a bunch of English / German religious  
zealots had landed at Plymouth Rock).

For those of you still left, who haven't rolled your eyes and just  
sent an angry letter to the moderator asking "when is enough enough",  
if you are involved in studying something as near and dear to hominids  
as the brain - I am afraid that one has to seek and consider the  
influence of that brain's own predispositions on its study of itself  
(as has been pointed out by philosophers for thousands of years).

But I don't raise this issue simply as a sophomoric exercise to invoke  
nostalgic feelings about college dorm rooms -- Earlier I asked what I  
consider to be a very serious and practical question -- what is the  
evidence that most of the behavior we ascribe to learning actually is  
due to the kind of learning mechanisms being studied by  
neurobiologists and dear to the hearts of the neural network and  
abstract modeling community?  Sure, we can force starved or thirsty  
monkeys to learn weird things (and do weird things), but how much of  
the real behavior of real brains in the real world has anything to do  
with the "learning" we hold near and dear to our hearts?  Neural  
network guys want stuff to learn, because it makes engineering easier  
(or does it??), but what do we really 'learn".  Evidence in the US at  
the moment, is not very much.

Cognitive psychologists know about this well - human phobias involve  
snakes and spiders, not electric outlets, despite the fact that  
electrical outlets are more numerous and more dangerous.  Although  
still described as 'learning' even in the "associative learning  
domain" it is much easier to train humans to associate pictures of  
snakes and spiders with aversive stimuli than more dangerous real  
world objects like electrical outlets.  As I pointed out with respect  
to the recent NSF report cited on this list -- the question of what we  
already "know" and how it governs our behavior, or even the extent to  
which what looks like 'learning' might actually be a different fixed  
read out, with changing context, is seldom considered by computational  
neurobiologists, or the neural network community.  We seem fixated on  
the idea that we are "learning machines" and largely lumps of clay  
fashioned by experience (especially again in the US -- Conrad Lorenz  
didn't think so at all).  This of course, is simply silly.  Please  
note that this question is actually deeply connected to the abstract  
vs realistic modeling issue -- if, in fact, the structure of the  
nervous system is highly patterned based on a long evolutionary  
history, THEN ignoring that structure when building models may  
profoundly miss the point.

To be even more specific, I now suspect that the olfactory system may  
actually have built into it, at birth, a great deal of knowledge about  
the detailed structure of metabolic systems in the real world --   
While the olfactory cortex has been considered for many years  
(including by me), as being as close to a pure associative learning  
network as can be found in the brain, in fact, I now suspect that the  
appearance of an unstructured highly interconnected pattern of  
neuronal connectivity, may actually be disguising a highly ordered set  
of connections reflecting prior knowledge about the metabolic  
structure of the real world.  This fundamental change in my own  
thinking was driven by results of an olfactory cortical model that  is  
probably the most complex realistic model that has so far been  
constructed.  Unfortunately, the work has  only been published in the  
form of a thesis   (M. Vanier,  Realistic computer modeling of the  
mammalian olfactory cortex.   http://nsdl.org/resource/2200/20080620191954527T 
  ).  Experimental evidence that the olfactory system might "know" a  
great deal about bio-metabolism is also only published in thesis form - 
Ruiter, Christine, “The Biological Sense of Smell: Olfactory Search  
Behavior and a Metabolic View for Olfactory Perception”, Ph.D.thesis,  
California Institute of Technology, Pasadena, CA,. But here is a link  
to some subsequent work http://www.inb.uni-luebeck.de/forschung/eops/INS2002.pdf 
   that you might find interesting.

The point being, that where the number of neurons are small  
(invertebrate systems), development produces highly order and  
reproducible networks.   Most mammalian neurobiologists have asserted  
that brains with large numbers of neurons have adopted a different  
strategy that is more flexible and plastic (read 'free will").  I am  
not so sure.  The complexity of the mammalian brain makes it very hard  
to ask the question.

One last point about free wheeling cognitive possibilities - I don't  
believe it for a minute.  In fact, I suspect that our cognitive  
structure is fundamentally linked to the computational problem faced  
by the olfactory system, and the computational solution it reached to  
solve that problem.  In other words in some computational / cognitive  
sense 'we sniff the world' whether we use visual data, auditory data,  
somatosensory data, or olfactory data.  For implications see  
(  Fontanini, A. and Bower, J.M.  (2006) Slow-waves in the olfactory  
system: an olfactory prospective on cortical rhythms.  Trends in  
Neuroscience.  29: 429-437 ).  Thus, in fact, I don't believe at all  
that we have huge cognitive freedom, I think we are fundamentally  
constrained by how the olfactory system "thinks".    Its just that our  
inordinate focus on the visual system (visual primates with large  
brains) has lead us seriously astray in thinking about how brains work.

Writing a book on this -- thanks for helping.

Jim Bower

On Aug 14, 2008, at 3:55 AM, Asim Roy wrote:

> Hi All,
> I read with interest this ongoing debate in the computational  
> neuroscience community. Not being a computational neuroscientist  
> myself, I was a little hesitant to wade into this debate. However, I  
> thought I would raise some issues of deep interest to me and try to  
> get some feedback from the community.
> My hunch is that one part of computational neuroscience is about  
> discovering the existing wiring and operating mechanisms of certain  
> parts of the brain that generally come predefined or prewired to us,  
> like parts of the vision system. Some modules that may not come  
> predefined or prewired are like the ones that a biologist has to  
> create in his/her brain to learn a bit of mathematics in graduate  
> school. That stuff is new for the brain and it's hard to learn. Wish  
> it came prewired.
> So I hope that there is another side of computational neuroscience  
> that looks at learning mechanisms within the brain. That's the side  
> that is of great interest to many of us who work on learning  
> algorithms. I was wondering if there are any new insights or  
> theories in computational neuroscience on how the brain "learns."  
> Here are some issues that I would love to get some feedback on:
> 1. The artificial neural network community still believes that  
> learning in the brain is real-time, almost instantaneous. It's real- 
> time in the sense of Hebbian-style learning. And I believe  
> computational neuroscience predominantly uses Hebbian-style models  
> of learning. I personally doubt that the brain learns in real-time.  
> There is plenty of evidence in experimental psychology to refute the  
> real-time learning (Hebbian-style synaptic modification) claim. And  
> there is also enough recent evidence in cognitive neuroscience too  
> to refute that claim, although one has to carefully read between the  
> lines the conclusions of these papers. (In one such case, I  
> suggested a different interpretation to the results and the authors  
> agreed with it.) One can also logically argue that real-time  
> instantaneous learning amounts to "magic" since no system,  
> biological or otherwise, can set up a network and start learning in  
> it without knowing anything about the problem before the start of  
> learning.
> My question is, is computational neuroscience still a firm believer  
> in Hebbian-style real-time learning or have researchers looked at  
> other forms of learning, like memory-based learning that is not real- 
> time?
> 2. It appears that the brain has the capacity to design networks  
> when a new skill has to be learnt. Are there any studies/insights in  
> computational neuroscience on how this design process works?
> 3. In machine learning and neural networks, there are two extremes  
> sides to designing of algorithms. At one end are back-propagation  
> type algorithms where neurons in the network use local learning laws  
> to learn. At the other end are Support Vector Machines (SVM) type  
> algorithms, which were mentioned in that NSF report, which bring in  
> heavy computational machinery (e.g. quadratic programming) to both  
> design and train neural networks. SVMs don't use local learning  
> laws. I don't believe we have SVM-style mechanisms in our brains;  
> it's just too complicated. So SVM algorithms are unrealistic for the  
> brain, although they are widely used to solve learning problems for  
> the machine learning community. But Hebbian-style or back- 
> propagation-style real-time learning also has problems and that's  
> not just with evidence from cognitive neuroscience and experimental  
> psychology, but logical ones too.
> The question here is related to the first one: Is there a way in  
> computational neuroscience to verify any of these theories of  
> learning?
> Hope I am not asking stupid questions. Would love to get some  
> thoughts and feedback. And any references would help.
> Best wishes,
> Asim Roy
> Arizona State University
> _______________________________________________
> Comp-neuro mailing list
> Comp-neuro at neuroinf.org
> http://www.neuroinf.org/mailman/listinfo/comp-neuro


Dr. James M. Bower Ph.D.

Professor of Computational Neuroscience

Research Imaging Center
University of Texas Health Science Center -
-  San Antonio
8403 Floyd Curl Drive
San Antonio Texas  78284-6240

Main Number:  210- 567-8100
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