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: during learning these neurons become tuned to relevant features and represent the information that is required later as a persistent elevation of their activity. It is however not well known how these neurons acquire their task-relevant tuning. Here we introduce a biologically plausible learning scheme that explains how neurons become selective for relevant information when animals learn by trial and error. We propose that the action selection stage feeds back attentional signals to earlier processing levels. These feedback signals interact with feedforward signals to form synaptic tags at those connections that are responsible for the stimulus-response mapping. A globally released neuromodulatory signal interacts with these tagged synapses to determine the sign and strength of plasticity. The learning scheme is generic because it can train networks in different tasks, simply by varying inputs and rewards. It explains how neurons in association cortex learn to (1) temporarily store task-relevant information in non-linear stimulus-response mapping tasks and (2) learn to optimally integrate probabilistic evidence for perceptual decision making.