[Comp-neuro] Re: Attractors, variability and noise

Chris Eliasmith celiasmith at uwaterloo.ca
Wed Aug 13 20:52:08 CEST 2008


Andrew Coward wrote:
> Understanding the brain will require an analogous hierarchy of 
> descriptions. In other words, we have to find good approximations at 
> several intermediate levels that can be mapped both into psychology and 
> into more detailed neuron type models. 

I couldn't agree more.  We have also proposed such a hierarchy and
methods for moving between 'levels' of description of neural
systems in our book (Eliasmith & Anderson, 2003).  Successfully 
accounting for some data depends on single cell dynamics (Singh & 
Eliasmith, 2006); accounting for other data depends
more on large-scale architecture (Litt et al, in press).
Accounting for all the data depends on both.

I'm quite disappointed at the 'detailed' vs 'abstract' debate --
both are relevant *depending on the explanatory target*.
*Unified* explanations will come by being able to move between
these levels of description smoothly (notably this has not yet
been achieved by physics either).

The centrality of mechanisms, hierarchies of such, decomposition
and synthesis of complex systems has become a clear
focus of recent philosophy of science (Craver 2004;
Bechtel & Richardson, 1993; all of which is far more relevant to
biology than Kuhn).  This emphasis eliminates the polarized
debate currently taking place on this list.

Regards,
Chris

Eliasmith, C. and C. H. Anderson (2003). Neural Engineering: 
Computation, representation and dynamics in neurobiological systems.
MIT Press.

Singh, R. and C. Eliasmith (2006). Higher-dimensional neurons explain 
the tuning and dynamics of working memory cells. Journal of 
Neuroscience. 26: 3667-3678.

Litt, Eliasmith, & Thagard, (in press) Neural affective decision
theory: Choices, brains, and emotions, Cognitive Systems Research 
Available online 14 April 2008. doi:10.1016/j.cogsys.2007
(http://www.sciencedirect.com/science/article/B6W6C-4S8TBBS-1/2/259722b8d5c60fb9906a76437cde38fb)

Bechtel, W. and Richardson, R. C. (1993). Discovering complexity: 
Decomposition and localization as strategies in scientific research. 
Princeton: Princeton University Press.

Craver (2007) Explaining the brain: mechanisms and the mosaic unity of 
neuroscience. Oxford University Press.


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