Combining probabilistic population codes.
In: Pollack, ME, (ed.)
IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2.
(pp. 1114 - 1119).
MORGAN KAUFMANN PUB INC
We study the problem of statistically correct inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the simultaneous activity of many units coding for some low dimensional quantity. A classic example are place cells in the; rat hippocampus: these fire when the animal. is at a particular place in an environment, so the underlying quantity has two dimensions of spatial location. We show how to interpret the activity as encoding whole probability distributions over the underlying variable rather then just single values, and propose a method of inductively learning mappings between population codes that are computationally tractable and yet offer good approximations to statistically optimal inference. We simulate the method on some simple examples to prove its competence.
|Title:||Combining probabilistic population codes|
|Event:||15th International Joint Conference on Artificial Intelligence|
|Dates:||1997-08-23 - 1997-08-29|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
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