Jay A. Hennig

A population code for semantics in human hippocampus

The paper investigates how semantic information is encoded in human hippocampal activity during speech processing, finding that distributed neural patterns capture semantic relationships similar to language models.

Dynamic belief representation and updating through learned attractor-like dynamics in the frontal cortex

The paper shows that OFC population activity in mice exhibits task-specific attractor-like dynamics during belief-state inference, mirroring Value-RNN dynamics.

Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech

The paper develops a pipeline to estimate neuronal tuning for word meaning from naturalistic speech and validates it using patient data and ground-truth controls.

A number simplex in the human medial temporal lobe

The paper identifies simplex-shaped neural manifolds for number representation in the human medial temporal lobe and finds similarities with large language models.

Polysemanticity in human hippocampal neurons

This study shows that human hippocampal neurons use polysemantic coding, where neurons represent multiple unrelated semantic features through high-dimensional superposition, enabling efficient contextualization of meaning.

Distinct tasks engage a shared neural subspace in human hippocampus and anterior cingulate cortex

The paper investigates how neural circuits in the human hippocampus and anterior cingulate cortex support multiple tasks, finding shared neural subspaces across distinct behaviors.

A geometric foundation for word meaning in the brain

The paper shows that semantic features in word meaning are encoded in the brain through geometric structures similar to those in language models.

Phasic dopamine drives conditioned responding beyond its role in learning

This study investigates the role of dopamine in conditioned responding, finding that it directly modulates responses beyond its role in learning.

Experience shifts value updating from synaptic plasticity to neural dynamics

The paper shows that experience shifts value updating from synaptic plasticity to neural dynamics, revealing a trade-off between memory stability and behavioral flexibility.

A theory of brain-computer interface learning via low-dimensional control

This paper develops a theory explaining BCI learning through low-dimensional control strategies, validated with experimental data.

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

This study investigates how dopamine signals in the ventral striatum relate to contingency during associative learning in mice, finding that TD learning models with ITI state representations explain the results better than contingency-based models.

Emergence of belief-like representations through reinforcement learning

This paper shows that recurrent neural networks can estimate value directly from observations without explicitly forming beliefs, generating reward prediction errors similar to experimental observations.

Learning alters neural activity to simultaneously support memory and action

The paper shows that learning a new BCI map alters neural activity in a way that preserves performance on a familiar map, suggesting a mechanism for learning multiple motor behaviors without interference.

Learning is shaped by abrupt changes in neural engagement

The paper investigates how internal states like arousal affect neural engagement during learning in non-human primates using a BCI paradigm, finding that abrupt changes in engagement influence learning speed for different task goals.