Blake Bordelon

This study explores how structured and disordered connectivity in recurrent neural networks affects population dynamics and task performance, finding that minimal restructuring can achieve task-relevant representations.

This paper develops a normative model of place field reorganization during reward maximization, showing it unifies navigation phenomena and improves policy learning.

This study shows how population codes shape inductive bias for efficient learning, demonstrating that biological codes achieve lower activity while maintaining task-specific efficiency.