Visually Guided Decisions Driven by Distinct Population Computations with Non-Sensory Neurons in Visual Cortical Areas

Abstract

It is widely assumed that trial-by-trial variability in visual detection performance is attributed to the fidelity of the visual responses in visual cortical areas influenced by fluctuations of internal states such as vigilance and behavioral history. However, it is not clear what neuronal ensembles represent such different internal states. Here, we utilized a visual detection task, which distinguishes perceptual states from identical stimuli, while recording neurons simultaneously from the primary visual cortex (V1) and the posterior parietal cortex (PPC). We found distinct population dynamics segregating hit responses from misses, despite no clear differences in visual responses. The population-level computation was significantly contributed by heterogeneous non-sensory neurons in V1, whereas the contribution from non-sensory neurons with previous outcome selectivity dominated in the PPC. These results highlight the non-trivial contributions of non-sensory neurons in V1 and PPC for population-level computation that drives behavioral responses to visual information.