Ari E. Kahn

This study shows that meta-learning in rats involves changes in prefrontal cortical dynamics that enable rule-based reward learning.

The paper investigates how neural responses differ during learning of modular and non-modular graph structures using fMRI data, finding distinct connectivity patterns associated with performance on different graph types.

The paper investigates how humans learn successor representations using trial-by-trial reaction times in graph sequence learning tasks, finding that eligibility trace mechanisms play a predominant role.

The paper shows that the structure of a temporal sequence affects how well stimuli are represented in the brain, with modular graphs leading to better prediction and higher dimensionality in visual areas.

The paper investigates how humans balance detailed and temporally abstract world models during learning, finding individual variation in strategy use and dynamic adaptation based on environmental predictability.

The paper shows that rat hippocampus generates flexible representations of alternative spatial locations during navigation, modulated by changing cognitive demands.

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

The paper shows dopamine signals in rats update value through two learning processes during foraging in a complex environment.

This study uses network control theory to examine how alprazolam affects persistence energy during emotion processing in schizophrenia relatives, revealing compensatory mechanisms and neuromodulatory relationships.

This study links cognitive map formation during sequence learning to neural activity in the temporal lobe using behavioral modeling and iEEG recordings.

This study uses network control theory to predictably reconfigure brain network interactions through targeted neurostimulation in humans.

This paper uses network control theory to model how white matter tracts guide electrical stimulation in the brain, showing that real white matter structures better predict state transitions than artificial models.

The paper shows that temporal structure in network traversal affects how well learners infer network architecture.