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

Ali Minai minai_ali at yahoo.com
Sat Aug 16 16:01:59 CEST 2008


Jim,

I think that your perspective is probably much closer to reality than those found in cognitive science, neural networks, or even in neuroscience. In particular, the point that our focus on learning is - at least in part - driven by our desire to explain free will is especially insightful. I'll make sure I use it as a reality check myself:-).

One factor that has complicated our understanding of living organisms and their behavior is the disciplinary cleavage between those who study evolution, those who focus on development and those who look at processes in individual organisms. Models of learning using neural networks typically start with tabula rasa networks, or networks with theoretically-defined structures (such as columnar and/or laminar organizations) that are thought to be more biologically plausible. However, this ignores the developmental process by which the actual networks in the brain are laid out, taking account of the way the organism has experienced the world's regularities in the developmental phase. And even those models that do take development into account (e.g., models including formation of orientation columns or developmental motor learning) do so in narrow domains. More importantly, they often neglect the evolutionary component, which determines the structures and
 constraints underlying the system that development shapes and learning re-shapes.

My opinion - which, I'll admit, dismays me somewhat - is that we'll never really understand how behavior arises from living organisms until we have an integrated framework for plasticity at all scales - from evolutionary change down to the action potential and lower. All of us as scientists and modelers live somewhere along this continuum, and may look a bit to either side, but are uncomfortable stepping back and taking in the whole range of scales. Such broad thinking is, I think, embedded to some degree in the embodied view of cognition, but in practice researchers end up focusing on a particular scale because of the need to produce concrete results in reasonable time.

I really like your sniffing brain approach. I am convinced that most - probably all - of what we consider learning is achieved through relatively simple changes in networks of multi-scale modules (that dreaded word!:-) configured by evolution and instantiated by development. The tabula on which learning occurs is anything but rasa. Systems biology is teaching us every day that organisms are much more lego-like than we thought. Why should the brain be an exception? And evolutionary biology shows us that more complex organisms arise more by multiplying, modifying, ramifying, rearranging and subsuming existing modules than by creating brand new ones from scratch. Then why should we not expect a human brain generating human behavior to be very similar to a rat or a lamprey brain generating those animals' behaviors - just more complexified through the processes I listed above? We implicitly acknowledge this when we focus on these animals as models, but we
 don't let go of the prejudice that "something more" will ultimately be needed to explain the behavior of "higher" animals. I, for one, am convinced that the behavior of the average voter (for example) can be explained completely by an invertebrate model:-).

Your book sounds really interesting. When is it out?

Ali

---------------------------------------------------------------------
Ali A. Minai
Complex Adaptive Systems Lab
Associate Professor
Department of Electrical & Computer Engineering
University of Cincinnati
Cincinnati, OH 45221-0030

Phone: (513) 556-4783
Fax: (513) 556-7326
Email: aminai at ece.uc.edu
          minai_ali at yahoo.com

WWW: http://www.ece.uc.edu/~aminai/

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--- On Fri, 8/15/08, james bower <bower at uthscsa.edu> wrote:
From: james bower <bower at uthscsa.edu>
Subject: [Comp-neuro] The sniffing brain - and free will
To: "Asim Roy" <ASIM.ROY at asu.edu>
Cc: comp-neuro at neuroinf.org
Date: Friday, August 15, 2008, 12:33 PM

Asim,
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

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