Combining Probabilistic Population Codes

We study the problemof statisticallycorrect 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 re 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 underlyingvariableratherthen just singlevalues, and propose a method of inductively learning mappings between population codes that are computationally tractable and yet o er good approximations to statistically optimal inference. We simulate the method on some simple examples to prove its competence. In a population code, information about some lowdimensionalquantity (such as the positionof a visualfeature) is represented in the activityof a collectiono...

• Type: Conference
• Year: 1997
• Where: IJCAI
• Authors: Richard S. Zemel, Peter Dayan
• Keywords: IJCAI 1997, IJCAI 2007, Population Codes, Population Codes Tanaka, Problemof Statisticallycorrect Inference