Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions.

ABSTRACT: Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions.

SUBMITTER: Chaisangmongkon W 

PROVIDER: S-EPMC5586485 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature