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Probabilistic interpretation of population codes

Zemel, RS; Dayan, P; Pouget, A; (1998) Probabilistic interpretation of population codes. NEURAL COMPUT , 10 (2) 403 - 430.

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Abstract

We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population. In casting it in the encoding-decoding framework, we find that this model is too restrictive to describe fully the activities of units in population codes in higher processing areas, such as the medial temporal area. Under a more powerful model, the population activity can convey information not only about a single value of some quantity but also about its whole distribution, including its variance, and perhaps even the certainty the system has in the actual presence in the world of the entity generating this quantity. We propose a novel method for forming such probabilistic interpretations of population codes and compare it to the existing method.

Type:Article
Title:Probabilistic interpretation of population codes
Keywords:POSTERIOR PARIETAL NEURONS, RESPONSES, NETWORKS, CORTEX
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit

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