Eghbal A. Hosseini

The paper maps human cortical connectivity using es-fMRI and finds that functional similarity, not distance, primarily determines anatomical connections.

This study shows that high-performing ANNs and brains converge on similar representations, and identifies stimuli that modulate model-to-brain alignment.

The paper identifies three distinct temporal receptive window sizes in neural populations of the language network using intracranial recordings.

The paper shows that language models trained on developmentally realistic data can predict human brain responses to language, achieving near-maximal performance with 100 million words of training.

The paper investigates how transformer models straighten neural sentence trajectories to improve next-word prediction, showing curvature decreases with model performance and linguistic context.

This paper shows that powerful transformer models predict neural and behavioral responses during language processing across datasets and modalities, supporting predictive processing as a core mechanism.

This paper investigates how reinforcement affects timing variability in humans and monkeys, identifying two distinct sources of variability and their neural correlates in thalamus.

The paper shows that sensorimotor computations in monkeys are controlled by dynamical system principles, using inputs and initial conditions to adjust behavior.

The paper investigates how neural networks in the medial frontal cortex of primates control timing flexibility by showing that firing rate profiles are temporally stretched or compressed for different intervals, with speed control originating within cortical networks.