Tony Zhang

This paper introduces an algorithm called endotaxis that enables navigation and goal-learning in complex environments by simulating chemotaxis-based internal odor guidance.

This study examines how mice learn complex maze navigation rapidly, finding that they achieve high learning rates through local turning rules rather than global memory.

The paper develops an automated platform for high-throughput mouse training and trial-by-trial behavioral analysis during learning, revealing hesitation through unsupervised trajectory analysis.