A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.

ABSTRACT: The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable lim

SUBMITTER: Zhou B 

PROVIDER: S-EPMC5155514 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature