[Comp-neuro] From Socrates to Ptolemy

jim bower bower at uthscsa.edu
Fri Aug 1 14:45:10 CEST 2008

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. . 



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.


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