Mark van Rossum, Institute for Adaptive and Neural Computation School of Informatics, University of Edinburgh | Biases in multivariate neural population codes

Abstract

Throughout the nervous system information is commonly 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 the 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 can emerge. Although the bias can be reduced by competitive coding and disappears in the complete absence of noise, the bias is remarkably persistent at low noise levels. We develop a general framework, based on Gaussian Processes, that allows for an accurate calculation of the bias and reveals that the distribution of estimates is bimodal. The results have strong implications for neural coding and behavioral experiments.

Literature

• Biases in multivariate neural population codes. Sander W. Keemink, Mark C. W. van Rossum. doi: https://doi.org/10.1101/113803 (Link)