[Comp-neuro] useful models

Neil Burgess ucganlb at ucl.ac.uk
Thu Aug 7 10:12:17 CEST 2008

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 Burgess,
> -----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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