Prefrontal cortex exhibits multi-dimensional dynamic encoding during decision-making

Abstract

Recent work has suggested that prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. Here we investigate population-level coding of decision variables in monkey PFC using a new method for identifying task-relevant dimensions of neural activity. Our analyses reveal that, in contrast to one-dimensional attractor models, PFC has a multi-dimensional code for decisions, context, and relevant as well as irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases, and show that they are not the product of distinct populations, but of a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a sustained plateau in decoding accuracy, revealing that rotational dynamics in PFC preserve sensory as well as choice information for the duration of the stimulus integration period.