Pratik Chaudhari

This paper introduces a unified recurrent network model that co-emerges grid and place cells without supervision or pre-existing spatial-cell representations, reproducing experimental observations.

This paper introduces a self-supervised method to measure dependencies between biological signals and highlights its limitations in capturing extraneous dependencies.

This paper proposes a system-level theory of MEC-HC wiring to explain how grid and place cell patterns enable cue-triggered goal retrieval and path planning, validated through simulations.

The paper shows that learning temporally regular features improves auditory object discrimination compared to conventional methods.

This study shows humans prefer simpler explanations for uncertain data, aligning with formal model selection theories that penalize excessive flexibility.