Probabilistic interpretation of
population codes.
Rich Zemel Peter Dayan Alex Pouget
Neural Computation, 10, 403-430.
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.
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