James C. R. Whittington

This paper develops a mechanistic model of the Prefrontal Cortex that unifies value and structure through behavioural flexibility, showing that value acts as a control signal for working memory representations.

This study shows that inferential planning in the frontal cortex generates simultaneous neural representations of sequence elements through probabilistic inference, unifying planning, memory, and motor preparation findings.

The paper develops an efficient computing theory to explain optimal representations in prefrontal cortex working memory tasks, showing compositional and contextual codes as extremes of a spectrum determined by task structure.

The paper identifies a neuronal mechanism in mice that maps abstract behavioural structure, enabling zero-shot learning of new tasks through goal-progress cells in the medial frontal cortex.

This paper develops a mathematical theory unifying hippocampal episodic memory and prefrontal working memory by linking memory storage in synapses to controllable neural activity slots.

The paper shows that hippocampal replay constructs compositional memories of future states by recombining existing primitives, enabling optimal behavior in novel environments without new learning.

The paper introduces the Tolman-Eichenbaum machine, a computational model that unifies spatial and relational memory by explaining hippocampal and entorhinal cell representations through generalisation principles.

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

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

This paper shows that a predictive coding network with local Hebbian plasticity can approximate error back-propagation for supervised learning without external control.