Timothy E.J. Behrens

This paper proposes a mechanistic theory of planning in prefrontal cortex using spacetime attractors, showing that recurrent neural networks trained on planning tasks recapitulate this structure.

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.

The paper investigates the emergence of abstract relational representations in the human medial prefrontal cortex over time using fMRI and graph-learning paradigms.

This study identifies neural signatures of model-based and model-free reinforcement learning in prefrontal cortex and striatum using single-neuron recordings in rhesus macaques.

This study identifies two distinct neural representations in mouse medial frontal cortex during flexible navigation, showing how they encode position and goal distance to guide behavior.

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 demonstrates that the human entorhinal cortex represents abstract structural knowledge in non-spatial tasks with hexagonal grid structure, but not in tasks with clustered structure.

The paper investigates how the brain executes programs by analyzing neural activity during mental program execution, finding that fast neural sequences represent candidate solutions.

The paper shows that ventromedial prefrontal cortex uses grid-like coding and theta frequency modulation to represent value space during economic decisions.

The paper investigates neural mechanisms of credit assignment for delayed outcomes using fMRI and an adaptive learning task, identifying roles for lOFC, HC, and lFPC in tracking causal choices over time.

The paper shows that replay-DMN coordination develops with age, predicting grid-like representations in the entorhinal cortex during memory formation.

The paper shows that hippocampal ripples during rest predict the emergence of grid-like codes in the brain for inferring novel relationships in a 2D conceptual space.

The paper shows that replay during rest integrates fragmented experiences into a coherent cognitive map, reducing the need for online computation during inference.

The paper shows that non-spatial grid-like neural codes in the entorhinal cortex develop with age and correlate with inferential reasoning and intelligence.

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.

The paper shows that a hierarchical coordinate system in the human entorhinal cortex supports abstract sequence memories, mirroring spatial memory organization.

The paper develops a neural network model, Sleep Interpreter (SI), to decode memory replay from sleep EEG and shows it can accurately detect reactivation during sleep that correlates with memory performance.

This study uses EEG-fMRI to examine brain-wide activation during neural replay, finding correlations between replay strength and hippocampal activity, medial prefrontal cortex activity, and connectivity to the default mode network.

This study shows distributional reinforcement learning better explains prefrontal cortex responses compared to classic reinforcement learning.

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 humans reactivate neural representations of past events at event boundaries during naturalistic narrative understanding, similar to offline replay in rodents.

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.

This paper introduces STRM, a method for analyzing M/EEG data to infer spatiotemporal activity patterns and improve predictive accuracy in neuroscience tasks.

The paper shows that the anterior cingulate cortex encodes the value of peripheral cues before fixation to guide subsequent fixations and choices during economic decision-making.

The paper shows that visual understanding involves relational and compositional representations in the prefrontal-hippocampal circuit, with replay sequences forming hypotheses about scene configurations.

The paper shows that non-invasive human recordings can reveal state space trajectories of choice processes, finding that choice-irrelevant features are less represented than relevant ones even after extensive training.

The paper shows that prefrontal cortex abstracts problem structure while hippocampus maps it to specific situations in mice during reversal learning.

The paper shows replay bursts occur during activation of the default mode and parietal alpha networks, coordinating information flow between cortical areas.

The paper shows that entorhinal and ventromedial prefrontal cortex representations generalize reinforcement learning problem structures across different tasks.

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.