[Comp-neuro] useful models
todd.troyer at utsa.edu
Wed Aug 13 05:25:37 CEST 2008
Biological systems follow systematic principles in building particularity on
top of particularity. We'll need to understand the particularities as well
as the principles to understand much of real interest. Finding the sweet
spot between the two is much of what makes good science. Overall, biology
is more dependent on the particularities than physics, and general
principles and 'big questions', while important, are less likely to take you
Jim's question provides a good example of the dilemma: 'If one believes in
the importance of wiring -- shouldn't we all be working in invertebrate
preparations?' Well if we're looking to extract general principles from a
system with fewer connections, the answer might be yes. But if wiring is so
important, the large differences between invertebrate wiring schemes and our
own suggest that the answer might be no if we want to discover how our own
brains work. (Of course 'wiring' isn't everything anyway, since the same
circuit can have many different functional states.)
On a different note, one place where I think we need to do a lot better as a
field is in taking the 'private insights' mentioned by Ted and making these
public so that the same insight isn't continually 'rediscovered.'
Currently, there is little effort made to systematically write down or even
discuss such theoretical/computational insights at a level of specificity
and generality that is most useful. Not saying it's easy...
This also relates to the question of 'bi-directionality' in computational
neuroscience education (between those with backgrounds in biological vs.
computational fields). I agree too much effort has been in the computation
to biology direction. The vast majority of computational neuroscience
doesn't rely on very sophisticated mathematics, and I find that many of my
mathematically trained students don't know the math well enough to
understand the proper generalizations to biological systems anyway. Those
on both sides of the divide could benefit from more attention to the
interface between biological and mathematical concepts (working this into a
curriculum is a whole different challenge.) Note that things truly do go
both ways. Several times I've run across theoretical papers where
mathematical mistakes in the paper could have been avoided by thinking
through the biological implications (not to mention making the paper more
On 8/11/08 11:05 AM, "Ted Carnevale" <carnevalet at sbcglobal.net> wrote:
> 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 too exclusive a definition of utility. Two examples will suffice.
> 1. As Jim Bower noted, a model can be useful by prodding
>>> one to quantify ones ignorence by indicating which of
>>> the parameters require more data.
> This happens quite commonly. In practice, such utility is often
> demonstrable _before_ the model has been implemented in computable
> or mathematically tractable form.
> 2. Perhaps even more frequent are private insights gained by
> constructing and using models, whether they be "realistically
> detailed" computational or simplified mathematical abstractions.
> The design of hypotheses and experiments to test them depend
> critically on the judgment and insight of the investigator.
> However, in a highly interdisciplinary field like neuroscience,
> many highly productive investigators have serious gaps of
> knowledge and understanding that can be revealed and remedied by
> modeling. That said, ego being what it is, don't expect to hear
> public confessions of private epiphanies.
> Comp-neuro mailing list
> Comp-neuro at neuroinf.org
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