Biases in multivariate neural population codes

Abstract

Throughout the nervous system information is typically coded in activity distributed over large population of neurons with broad tuning curves. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of an encoded stimulus can be read out without bias. Here we find that when multiple stimuli are simultaneously coded in the population, biases in the estimates of the stimuli and strong correlations between estimates can emerge. Although bias produced via this novel mechanism can be reduced by competitive coding and disappears in the complete absence of noise, the bias diminishes only slowly as a function of neural noise level. A Gaussian Process framework allows for accurate calculation of the bias and shows that a bimodal estimate distribution underlies the bias. The results have implications for neural coding and behavioral experiments.