Linearly decodable functions from neural population codes.

The population vector is a linear decoder for an ensemble of neurons, whose response properties are nonlinear functions of the input vector. However, previous analyses of this decoder seem to have missed the observation that the population vector can also be used to estimate functions of the input vector. We explore the use of singular value decomposition to delineate the set of functions which are linearly decodable from a given population of noisy neurons.