Neurally Plausible Reinforcement Learning of Working Memory Tasks

A key function of brains is undoubtedly the abstraction and maintenance of information from the environment for later use. Neurons in association cortex play an important role in this process: by learning these neurons become tuned to relevant features and represent the information that is required later as a persistent elevation of their activity [1]. It is however not well known how such neurons acquire these task-relevant working memories. Here we introduce a biologically plausible learning scheme grounded in Reinforcement Learning (RL) theory [2] that explains how neurons become selective for relevant information by trial and error learning. The model has memory units which learn useful internal state representations to solve working memory tasks by transforming partially observable Markov decision problems (POMDP) into MDPs.