[Comp-neuro] useful models and the scientific method

james bower bower at uthscsa.edu
Tue Aug 12 16:50:35 CEST 2008


So here is a question -- where is the balance here?

A major focus of the original Methods in Computational Neuroscience  
Course in Woods Hole was to teach biologists about computation.   
However, for some reason, these courses seem to always drift in the  
direction of exposing physicists, computer scientists, mathematicians,  
etc to biology.  Then there are whole funding programs, like the Sloan  
Program several years ago for example, that are explicitly based on  
the notion that we need to expose more physicists, computer  
scientists, mathematicians, etc to biology.  It seems to me that the  
early history of physics suggests that a love of experimentation and a  
willingness to poke biology should come first, and the training in  
math, computation, etc, should come second.  That is not to say that  
it isn't possible for individuals to start one place and go the  
other.  There are many examples.  But many of our training programs  
have seemed to me to be built on a "have mathematics will travel"  
structure, on the assumption that it is easier to teach physicists  
biology than vice versa.

I am not so sure.

Jim


On Aug 12, 2008, at 6:18 AM, Neil Burgess wrote:

> I completely agree that the progress of science, via the interaction
> of theory and experiment, is greatly helped by people willing to
> get seriously involved in both modeling and experiments. They
> are best placed to create/experience the most useful interaction.
>
> Programs to facilitate 'interdisciplinary' science sometimes seem to  
> ignore
> individual researchers 'in the middle' in favor of encouraging  
> separate
> groups in either field to talk to each other.
>
> Best wishes,
>
> Neil
>
> Neil Burgess
> ICN, UCL
>
>> -----Original Message-----
>> From: comp-neuro-bounces at neuroinf.org [mailto:comp-neuro-
>> bounces at neuroinf.org] On Behalf Of james bower
>> Sent: 11 August 2008 17:20
>> To: n.burgess at ucl.ac.uk
>> Cc: comp-neuro at neuroinf.org
>> Subject: Re: [Comp-neuro] useful models and the scientific method
>>
>> In the early days of physics becoming what Kuhn* called a  
>> paradigmatic
>> science (
>> http://en.wikipedia.org/wiki/The_Structure_of_Scientific_Revolutions)
>> , or in other words, a science where a theoretical substrate exists
>> that can serve as the basis to knit experimental and theoretical work
>> together (or even force them together I would suggest), most
>> practitioners were both experimental and theoretical.  I would claim
>> that the segmentation between experimentalists and theorists that we
>> tolerate today in biology is another of the unfortunate consequences
>> of the success of modern physics - and the effort to apply the
>> pedagogy of modern physics to Biology, which is still fundamentally a
>> pre-paradigmatic science (by Kuhn's definition).  I believe that the
>> lack of an underlying theoretical structure in biology makes the
>> segmentation into theoretical and experimental tracks very difficult.
>>
>> Following up on Todd Troyer's remarks (Todd incidentally being a
>> theorist who decided it was essential to also be an experimentalist),
>> I agree that many if not most scientists of all types don't really
>> have much of an understanding about how science really works - in  
>> this
>> case codified in something we teach to 5th graders as "The Scientific
>> Method" and actually seem to believe ourselves.  And also agree, you
>> have to look no further than expectations at NIH or other funding
>> agencies for "hypothesis-driven research" to see that they also don't
>> understand how science really works.  The "hypothesis" that are being
>> tested are most often not much more than the 'tyrannical ideas"  that
>> abound in neuroscience (cortical columns, Marr/Albus cerebellar
>> learning, the significance of synchrony in neuronal coding, etc etc).
>> Unfortunately, few and far between are PhD requirements or even
>> courses in epistemology or history of science.  As Kuhn and other
>> philosophers of science have pointed out, bending history seems to be
>> an essential aspect of how science works, whether paradigmatic or  
>> not.
>>
>> For several years I have taught a course to graduate students called
>> "The History of Your Science".  In the course, students pick a  
>> classic
>> paper in their field, then we read the paper together (a significant
>> accomplishment in and of itself -- how many of you have, for example,
>> actually read Donald Hebb's book (which could never be published
>> today), or the classic papers referenced in the first or last
>> paragraphs of almost all scientific papers?).  Then in the second
>> section of the course, we read several modern papers referencing the
>> classic work.  I actually start the course off myself reading
>> Mountcastle's original cortical column papers.  Needless to say, the
>> scientific process looks much different than most students expect.
>>
>> Finally, I appreciate your continued indulgence - but these themes  
>> are
>> also considered in a review I wrote last year for "The American
>> Scientist" of a book titled "23 Problems in Systems Neuroscience"
>> which resulted from a meeting organized in 2000 whose purpose was to
>> generate a "roadmap" for neuroscience comparable to Hilbert's effort
>> in mathematics 100 years earlier.
>>
>> http://www.americanscientist.org/bookshelf/pub/math-envy
>>
>> In my opinion, the articles in that book make it very clear how far  
>> we
>> have to go in Computational Neuroscience.
>>
>>
>> Jim Bower
>>
>>
>>
>>
>> On Aug 7, 2008, at 3:12 AM, Neil Burgess wrote:
>>
>>> Re: the discussion of 'realistic' and 'useful' models.
>>>
>>> In practice a useful model is one that makes predictions which
>>> are novel and feasible enough to convince an experimenter
>>> to actually test them. This is actually quite rare, and
>>> may not have a simple dependence on either the level of
>>> biophysical detail or the mathematical elegance of the model.
>>> (The same is true, in reverse, of useful experiments:)
>>>
>>> Best wishes,
>>>
>>> Neil
>>>
>>> Neil Burgess,
>>> ICN, UCL.
>>>> -----Original Message-----
>>>> From: comp-neuro-bounces at neuroinf.org [mailto:comp-neuro-
>>>> bounces at neuroinf.org] On Behalf Of jim bower
>>>> Sent: 01 August 2008 13:45
>>>> To: bard at math.pitt.edu
>>>> Cc: comp-neuro at neuroinf.org
>>>> Subject: Re: [Comp-neuro] From Socrates to Ptolemy
>>>>
>>>> Ah Bard,  here I was happily headed back to the ranch (literally)
>>>> willing
>>>> to let the conversation die back ...  But ...
>>>>
>>>> Obviously, a useful model is a useful model regardless. And good
>>>> science
>>>> is good science regardless.  however, it is clear from the  
>>>> history of
>>>> science that different approaches come with different costs and
>>>> benefits,
>>>> and that different approaches are more or less useful depending on
>>>> the
>>>> state of the field. I believe that neuroscience today is more like
>>>> physics
>>>> in the 16th century than like physics in the 21st, and needs to go
>>>> through
>>>> a similar process of finding the appropriate methods for the
>>>> appropriate
>>>> questions. As then, I think that accomplishing those objectives  
>>>> will
>>>> require that we stay very close to physical reality (as Newton  
>>>> did in
>>>> using the moon's movement around the earth to both invent (or
>>>> borrow) the
>>>> calculous and discover the inverse square relationship in
>>>> gravitational
>>>> attraction).
>>>>
>>>> But, of course, then Newton and his predicessors especially, were
>>>> stacked
>>>> up against the methods, sucess, and vested interests of the  
>>>> catholic
>>>> church. In some ways I feel we in computational neuroscience are
>>>> similarly
>>>> stacked up against the high priests of science, the physicists, and
>>>> their
>>>> tried and true methods and no doubt valuable set of lessons
>>>> learned. But,
>>>> biology is different and the difference and the conflict is perhaps
>>>> best
>>>> indicated in the difference between abstracted models and  
>>>> "realistic"
>>>> models.
>>>>
>>>> First, I would define realistic models not only as those that
>>>> include as
>>>> much of the actual structure as possible, but also and perhaps most
>>>> importantly, models that are "idea" nuetral in their  
>>>> construction. Of
>>>> course I know that in the absolute there is no such thing, but
>>>> there is a
>>>> fundamental difference, for example, in taking 4 years to get a
>>>> Purkinje
>>>> cell model to respond as a real Purkinje cell to current injection,
>>>> than
>>>> starting by assuming Purkinje cells are Marr/Albus learning nodes  
>>>> and
>>>> proceeeding to build the model accordingly.
>>>>
>>>> Second with respect to the 4 years to build the initial model (and
>>>> up to
>>>> now the almost 15 years and counting to understand it), for physics
>>>> and
>>>> abstract models, the larger the number of parameters, in principle,
>>>> the
>>>> easier it is to get the model to do what you want (many famous
>>>> quotes on
>>>> this). In contrast, in realistic models, the larger the number of
>>>> parameters, the harder it is to get what you want. Further, whether
>>>> one
>>>> knows the exact value of the Kchannel conductances or not, one
>>>> knows for
>>>> sure the likely range, and therefore both GENESIS and NEURON can
>>>> provide
>>>> constraints and in effect alerts to parameters widely out of range.
>>>>
>>>> But most probably important for the power of realistic models, they
>>>> almost
>>>> immediately allow one to quantify ones ignorence by indicating
>>>> which of
>>>> the parameters require more data. Being realistic, the requested
>>>> data is
>>>> already in a form that, in principle, can be directly addressed
>>>> experimentally (I.e. What is the spatial relationship between
>>>> excitation
>>>> and inhibition on the small dendrites of the Purkinje cell.).  That
>>>> said
>>>> one of the tricks in realistic modeling is often using the model to
>>>> figure
>>>> out how to get at a critical parameter indirectly, even if there is
>>>> currently no experimental technique to get at it directly.
>>>>
>>>> Thus, as in physics then and now, the real value of all models
>>>> should be
>>>> to organize experimental science and force experimentalists (and
>>>> modelers)
>>>> to develop new techniques. The more realistic the model, the more
>>>> immediate the translation to reality.
>>>>
>>>> I will say again, however, if the assumptions of function are  
>>>> already
>>>> built into the model, this is much less likely to happen.
>>>>
>>>> So models are a device to get from here to there. Realistic models
>>>> make
>>>> the effort to have this path directed by the structure itself.
>>>> Abstract
>>>> models have often only begat new abstract models (almost all, as
>>>> iin the
>>>> historical case of Ptolemy, more complex than the previous). I hope
>>>> we can
>>>> avoid needing to reach the point as happened in the early history  
>>>> of
>>>> modern physics, that the shift to realistic models was driven by
>>>> the fact
>>>> that the abstract model had become more complex than the realistic
>>>> alternative.
>>>>
>>>> Finally, again, the purpose of modeling should not primarily be to
>>>> demonstrate what we know or believe, but to reveal our ignorence
>>>> and then
>>>> direct our progress towards reducing that ignorence . Realistic
>>>> models in
>>>> our hands have always helped us to understand that we know less
>>>> than we
>>>> even thought we did when we started building the model. .
>>>>
>>>> Jim
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> I promote and defend realistic modeling. think appropriate methods
>>>> it is
>>>> an interesting and important question,
>>>> ------Original Message------
>>>> From: G. Bard Ermentrout
>>>> To: James Bower
>>>> Cc: comp-neuro at neuroinf.org
>>>> ReplyTo: bard at math.pitt.edu
>>>> Sent: Aug 1, 2008 6:28 AM
>>>> Subject: Re: [Comp-neuro] From Socrates to Ptolemy
>>>>
>>>> We've established that there is no "noise" in the nervous system.
>>>> Now lets
>>>> take on the shibboleth of "realistic" models. So, I will ask you  
>>>> all
>>>> why a model with 10000 compartments with dozens of active channels,
>>>> none
>>>> of which has been measured (or probably can be with current
>>>> techniques) is
>>>> more realistic than an abstracter model about which one can prove
>>>> or argue
>>>> with some rigor is capable of explaining the underlying phenomena.
>>>> I think
>>>> one can easily go to far in simplifying, but one can also err in  
>>>> the
>>>> opposite direction.
>>>>
>>>> Bard
>>>>
>>>>
>>>>
>>>> Sent via BlackBerry by AT&T
>>>
>>> _______________________________________________
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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
>> 8403 Floyd Curl Drive
>> San Antonio Texas  78284-6240
>>
>> Main Number:  210- 567-8100
>> Fax: 210 567-8152
>> Mobile:  210-382-0553
>>
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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
8403 Floyd Curl Drive
San Antonio Texas  78284-6240

Main Number:  210- 567-8100
Fax: 210 567-8152
Mobile:  210-382-0553

CONFIDENTIAL NOTICE:
The contents of this email and any attachments to it may be privileged  
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