Timothy E.J. Behrens

The paper shows that reverse sequential replay supports non-local reinforcement learning in humans and is prioritized based on future utility.

The paper shows that humans can transfer structural knowledge across tasks by inferring abstract basis sets, improving performance in novel environments.

This study combines model-free and model-sensitive reinforcement learning in rhesus monkeys, showing both strategies influence decision-making and response vigor.

The paper shows that successful retrieval of episodic memories involves compressed sequential replay of memory elements, which direction depends on task goals.

This paper introduces TDLM, a toolkit for analyzing neural activity sequences, showing its ability to detect human replay during rest using MEG data and parallels to rodent hippocampal replay.

This study identifies two mechanisms involving the hippocampus and neocortex that protect against memory interference by organizing contextual representations and modulating neocortical inhibition.

The paper proposes that grid-like codes can encode global navigation information through cognitive maps, enabling intuitive planning without extensive lookahead.

The paper demonstrates a triple dissociation of attention and decision computations across three prefrontal cortex subregions during value-guided choice.

The paper reviews evidence and theories suggesting cognitive maps organize knowledge across domains, supporting flexible behavior and abstract learning.

The paper shows that four brain regions represent value estimates on different time-scales, interacting to support flexible decision-making.

This paper explores the role of inhibitory engrams in perception and memory, showing they regulate behavior and memory storage through balanced neural activity.