Christian D. Márton

The paper shows mice seek information for its intrinsic value, with distinct neural representations in the orbitofrontal cortex for information and reward.

TRAKR classifies neural time-series patterns with high accuracy using one-shot learning, outperforming distance metrics and matching deep supervised methods.

This study develops an RNN model of the dlPFC-dSTR circuit to investigate how learning shapes neural representations and action selection in a sequence learning task.

The paper examines cross-frequency coupling in macaque auditory cortex to identify patterns of top-down and bottom-up information processing, finding distinct frequency interactions for each direction.