Andrea Banino

This paper develops a deep-learning framework to decode sensory and behavioural variables from wide-band neural data with minimal user input and generalization across stimuli and brain regions.

Training a network only to predict its visual input makes head direction, boundary vector, place and egocentric boundary cell responses appear, and lets agents reuse behaviours in unfamiliar environments.