[Comp-neuro] Re: Attractors, variability and noise

james bower bower at uthscsa.edu
Fri Aug 15 20:15:52 CEST 2008

Is this how string theory or cosmology works in Physics?

Isn't the big lesson of non-euclidian geometry (as historically the  
first example) and physics as a whole, that formal systems free you  
from reliance on human intuition?

Can you really 'visualize' a world in which parallel lines always cross?

Who cares if single individuals understand?  Single individuals don't  
understand much of anything these days -- really --  Further, who says  
that everyone should understand or can understand?  Why does biology  
and neuroscience have to live by some egalitarian - one for all all  
for one standard for explanation.  Hate to say, but one of the reasons  
that many experimental neurobiologists don't like theory, is that it  
bothers them that they don't have the skills to participate (thus the  
need to completely change how biologists are educated).  Of course,  
there are also abstract modelers that don't like detailed  
compartmental models for much the same reason (too much biological  
detail -- inelegant, etc).  Then there are those who intentionally  
make their models so complex (and hide their base code) to reinforce  
the idea that they and they alone are smart enough to figure these  
things out.  Seems to me that one way or another, as in physics to a  
remarkable extent, we have to get to the point that these ego driven  
human frailties don't matter -- But by embracing them, and taking them  
as a standard for what is useful explanation, we are all just agreeing  
to dance around the camp fire (ppt) and tell interesting stories.   
That's not science, and won't work.

Yes, this is an issue of the nature of explanation, and the nature of  
the tools that lead to that explanation.

Ironically enough, although it annoys me at times, most of the funding  
for neuroscience research in the US comes from the National Institutes  
of Health.  They could not care less (in principle) about grand  
theories of brain function , because their focus is (again in  
principle) on understanding and improving human health.  Wouldn't a  
complete physical model of the brain -- be a useful device to  
understand the mechanics of disease?

Personally, I also study brains for more etherial reasons (the chase,  
they are beautiful, get to hang out with smart graduate students,   
etc), but, it just seems bizare to me to justify models of brains that  
may have nothing to do with brains because it is relatively easy to  
tell lots of people about them.

The planets would still circle the earth if that were the standard  
applied to physics, and we would know nothing about gravity.

It is very unlikely that the brain will ever be understood by 'the  
people' -- but accurate models of the brain will be very useful for  
people anyway, and lots of fun to build.

The core point remains, however, in a system as complex as the brain,  
how can you possibly ignore its machinery if we are trying to  
understand how that machinery works?  It is simply too easy for  
unrecognized biases and assumptions to sneak into other levels of  
explanation, justified by selective reference to simple ideas about  
the machinary.  In the end, 'the neurons represent the truth" and  
probably will make fools of us all.


On Aug 15, 2008, at 12:23 PM, Brad Wyble wrote:

> On Wed, Aug 13, 2008 at 7:23 PM, Robert Cannon <robert.c.cannon at gmail.com 
> > wrote:
> As I understand the  other end of the spectrum, we construct  
> increasingly realistic models and end up with a simulated brain  
> without a real understanding of how it works, which makes no sense  
> to me.  Understanding is what we're after, and that understanding  
> can only reside in the brains of the population of scientists, not  
> in their models.
> Brad's point is fascinating - not least because I couldn't disagree  
> more. :)
> I do like the notion of understanding, but I suspect it is also  
> somewhat
> self-indulgent, because there may not be a level on which it can be  
> shared
> above that of working models.
> But It is still the people, and not the models, that possess the  
> understanding.   If I were to email you my theory, encapsulated in a  
> functioning model of the hippocampus using millions of 500  
> compartment neurons, you would still need to execute the model and  
> build an understanding of its function in your head, in order to  
> have any useful thoughts about it.    To do that, I would have to  
> tell you my theory in verbal form, and how to find evidence of it  
> within the model's behavior.
> For complex behavior, that verbal formulation has to be high enough  
> level to capture the dynamics of interest in the behavior.   I mean,  
> do channel dynamics have much to say about language production?  And  
> if not, why use a supercomputer to simulate them?
> [snipping Astronomy analogy]
> My point is that for this particular problem, high-level theory is
> not much use. Some of it is epiphenomenal, and the rest is just plain
> wrong. The models work fine but they are too complicated to run in
> your head. The simpler things that you can run in your head or on
> paper are too coarse to be any use.
> I agree with you completely.  One cannot run the model in one's head  
> because even a simple equation can have surprising dynamics.  But  
> abstract modelling does not involve thinking in one's head, it just  
> involves a level of simulation that averages over low level details,  
> much as you already average over  Brownian motion.
> But one does need to *have* a high level theory to understand  
> behavior.  The back and forth between the scientist and the model is  
> what generates progress, not the mere existence of the model.


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

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