[Comp-neuro] useful models and the scientific method

G. Bard Ermentrout bard at math.pitt.edu
Wed Aug 13 04:33:05 CEST 2008


Having also been involved in the MCN course for the last 16 years, and 
having also seen classes from this course who are biologists and 
theoreticians, I find that there are some cases where the biologists have 
had sufficient mathematics/statistics to do computational neuroscience in 
a serious manner. However, in my experience, many (although not the ons 
that read this list) biologists got into biology precisely because it 
required the least amount of mathematics/physics of the sciences and 
there exists some antipathy toward theory as if it would somehow destroy 
the mystery of life.  I do not see reciprocal  aversion in physicist for 
biology although there is a core of mathematicians who dislike it - but 
in fact, they dislike any applications of mathematics. It is absolutely 
necessary for any aspiring theorist to be far more conversant in biology 
than vice versa.

-bard



On Tue, 12 Aug 
2008, james bower wrote:

> 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
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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 or
> contain privileged and confidential information. This information is only
> for the viewing or use of the intended recipient. If you have received this
> e-mail in error or are not the intended recipient, you are hereby notified
> that any disclosure, copying, distribution or use of, or the taking of any
> action in reliance upon, any of the information contained in this e-mail, or
> any of the attachments to this e-mail, is strictly prohibited and that this
> e-mail and all of the attachments to this e-mail, if any, must be
> immediately returned to the sender or destroyed and, in either case, this
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