[Comp-neuro] Education

james bower bower at uthscsa.edu
Wed Aug 13 18:02:35 CEST 2008


Realizing that this is an international list, and that many countries  
in the world do a much better job (vastly) in math and science  
education than the United States, let me still make the claim that the  
future of our field will depend on closing the loopholes in biology  
education that allow biology majors to avoid mathematics and the "hard  
sciences".  BUT, and it is an important BUT, fixing this problem  
doesn't involve simply pushing our students to take only the standard  
courses offered in other departments.  Even at Caltech, where all  
undergraduates are required to take a year of advanced mathematics and  
a 2 year sequence in physics, the math capable biology undergraduates  
endlessly complained that the math (and physics) they were required to  
take was often irrelevant to biology, and there were right.

So, WE have work to do.

Let me go a bit further - the problem of math and science education  
can't actually be fixed focusing only on college level courses (and  
certainly not focused on graduate level courses).  The truth is that  
we need to change the way that math and science are presented and  
taught starting in the earliest levels of our educational systems.  As  
I pointed out briefly previously, the standard description of the  
"Scientific Method" that we all know in our heart of hearts is bogus,  
is a feature of the introduction to every text book in the US starting  
in 1st grade.  In the US, almost every high school student is required  
to take biology - in fact, in many states that is the only science  
course high school students  take.  There is essentially no math, no  
theory, no modeling in any high school biology text book or course -  
instead the approach to teaching is essentially religious with  
textbooks equivalent to the bible, and also increasingly has no  
experimental component at all (no money).

But even high school is too late.  Those who for whatever reason, want  
to reverse scientific progress, defeat a scientific view of the world,  
and revert to a centralized dogmatic, and controlling political/ 
religious ideology, KNOW that the place to start is with 6 year olds.   
And they are working very hard in the US (and with growing influence  
in other parts of the world as well) to do so.  To the extent that our  
own approach to science education is dogmatic, centralized,  
regurgiative, we aid and abet their efforts.

Some of you know that in addition to my personal commitment to  
infrastructure and education at the undergraduate, graduate, and  
higher levels of biology education, I have also been very actively  
involved for many years in early science education.

It may (or may not) be comforting for you to also know that the  
virtual learning world that I started 9 years ago, as one of the first  
virtual worlds on the Internet, Whyville.net, is about to surpass 4  
million registered users world wide, average age 12 and 68% female.   
The 3.5 hours spent by the average user in Whyville on the site per  
month, makes Whyville one of the 5 stickiest sites on the entire  
Internet.  We have now also established a joint venture with the  
Spanish government to bring Whyville to Spanish language and culture,  
in Europe and Latin America.

Over the last year, we have introduced bioinformatics and  
computational biology to the site, in the form of a new biotechnology  
initiative.  250,000 children have engaged in this new activity so  
far.  As I said, it may or may not be comforting for you to know that  
a major focus of my current effort to transform biological science is  
focused on millions of 12 year olds -- my objective -- to change  
biology (just as I believe we need to understand the brain) from the  
bottom up.

:-)

Jim Bower





On Aug 12, 2008, at 9:33 PM, G. Bard Ermentrout wrote:

> 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
>>>>> _______________________________________________
>>>>> 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
>>>> 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
>>>> e-mail and all attachments to this e-mail must be immediately  
>>>> deleted
>>>> from
>>>> your computer without making any copies hereof and any and all hard
>>>> copies
>>>> made must be destroyed. If you have received this e-mail in error,
>>>> please
>>>> notify the sender by e-mail immediately.
>>>> _______________________________________________
>>>> 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
>> 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
>> e-mail and all attachments to this e-mail must be immediately  
>> deleted from
>> your computer without making any copies hereof and any and all hard  
>> copies
>> made must be destroyed. If you have received this e-mail in error,  
>> please
>> notify the sender by e-mail immediately.
>>
>>
>>
>>
>>
>>
>>
>>
>> _______________________________________________
>> 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
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
e-mail and all attachments to this e-mail must be immediately deleted  
from
your computer without making any copies hereof and any and all hard  
copies
made must be destroyed. If you have received this e-mail in error,  
please
notify the sender by e-mail immediately.










More information about the Comp-neuro mailing list