Intelligence is our ability to learn appropriate responses to new stimuli and situations.
Neurons in association cortex are thought to be essential for this ability. During learning
these neurons become tuned to relevant features and start to represent them with persistent
activity during memory delays. This learning process is not well understood. Here we
develop a biologically plausible learning scheme that explains how trial-and-error learning
induces neuronal selectivity and working memory representations for task-relevant …

The reference paper introduces a new reinforcement learning model called Attention-Gated
MEmory Tagging (AuGMEnT). The results presented suggest new approaches in
understanding the acquisition of tasks requiring working memory and attentional feedback,
as well as biologically plausible learning mechanisms. The model also improves on
previous reinforcement learning schemes by allowing tasks to be expressed more naturally
as a sequence of inputs and outputs. A Python implementation of the model is available on …