Combining Probabilistic Population Codes
Rich Zemel Peter Dayan
In IJCAI 15, 1114-1119.
Abstract
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.
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