Pietro Perona

Naive mice learn a 9-turn route in a reconfigurable 3D maze within about 20 rewards; mutants lacking neocortex and hippocampus also learn, remember and generalize, though early exploration is inefficient.

The paper shows that distinct, anatomically distributed neuron ensembles in the hypothalamus encode social and fear behaviors through mixed selectivity, with strong localization bias.

Bounding boxes from a transformer detector let the Segment Anything Model segment cells automatically; one generalist model beats generalist Cellpose and matches inter-annotator agreement across mammalian, yeast and bacterial images.

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

The paper introduces MARS, a software pipeline for automated analysis of social behaviors in mice, achieving human-level performance in pose estimation and behavior classification.

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.