The structure of the population code in neural ensembles of V4 shapes pair-wise interactions on different time scales.

Abstract

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match to sample task on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decoded correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons in most informative and less informative, and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled and correlated than less informative neurons. As neurons are divided in two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling and correlations within the coding pool are stronger than across coding pools. HighlightsO_LIIn a match-to-sample visual task, responses of neural populations in V1 and in V4 predict the stimulus class better than chance. C_LIO_LIIn V4, informative neurons are more strongly coupled, correlated and synchronized than less informative neurons. C_LIO_LIIn V4, neurons are more strongly coupled, correlated and synchronized within coding pools compared to across coding pools. C_LIO_LICorrelations within coding pools harm the performance of the classifier in both V1 and V4. C_LI