[Comp-neuro] good models versus bad models versus realistic models

Paul Miller pmiller at brandeis.edu
Wed Aug 13 20:39:50 CEST 2008


Hi,

I love this debate and thought I'd chime in here on the idea of assuming 
"how things work".

> Haven't abused at all -- with one big exception -- realistic models  
> are more likely to tell you how things work, than are models in which  
> 'how things work' is assumed.

But once a realistic model of a single cell has told us "how things 
work" at the cell level then if one is interested in what arises from 
the interactions of multiple cells --- "how things work" at the systems 
level --- it makes sense to use the simplest single cell model that 
captures how that  cell works (which for those interested in the system 
I think means "what makes the cell release neurotransmitter or 
neuromodulator").

Especially since, using your example, every single Purkinje cell in the 
brain is different with different sets of conductances, so that in all 
likelihood an "average" Purkinje cell in terms of conductances and 
morphology may not act like an "average" Purkinje cell in terms of 
function. So a detailed model of a set of Purkinje cells would need to 
make each cell different (in which case which model cell is the right 
one?) but probably have them function in the same way. I would argue 
that in biology (convergent evolution etc) it appears it is function 
that matters (for survival and reproduction which is really all that 
matters) not necessarily the detailed instantiation of that function.

So, agreed, at one level of research, let's ensure with whatever methods 
available (experimental of computational) that we have an accurate and 
correct description of single-cell function. However, at the next level 
(with an aim to understanding behaviour) let's maintain that *correct* 
function in the simplest possible model to understand its effect on the 
system.

I think it it is unfair to tar the outcomes of "simple" models with the 
outcomes of "wrong" models. Or is your claim that we are sufficiently 
ignorant of what makes any cell in the brain fire a spike, that a simple 
model of any cell's function is almost certainly wrong (as of now)?

Interestingly, your mentioning of the impossibility for one person to 
fully understand collaborative software efforts links in here. The key 
to such programming efforts is to maintain a modular structure. One 
person understands a module well enough to guarantee that a given set of 
inputs produces a given set of outputs. Another person need not 
understand how the module works, but can rely on its input-output 
relationship to connect it with other modules and produce a larger 
functioning module and ultimately (through the hierarchy) a functioning 
system. Of course if one has an incorrect description of the 
input-output characteristics of a module one is doomed, but that does 
not imply one needs a detailed description of every module. Furthermore, 
from the top-down, one can come to realize the system will only work if 
it has a particular type of component or module -- and can go looking 
for the existence of such a component.

In summary we all assume "how things work" at one level and based on 
these assumptions try to explain or predict "how things work" at a 
higher level. We choose at which level we work (maybe you think some 
choose to waste their time?) and our goal is to ensure we use the 
simplest of the correct models of any level we wish to integrate.

Paul.



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