This NIH-funded project aims to uncover circuit-level mechanisms underlying adaptive learning. The project involves computational modeling at different levels (mean-field to spiking networks) as well as analyses of behavioral, electrophysiological, and calcium imaging data in rats during dynamic learning and decision-making tasks.
Applicants should have a strong quantitative background (e.g. physics, math, statistics, computational neuroscience, computer science, etc), good programming skills, genuine interest in understanding the brain, and a collaborative attitude. A background in neuroscience is desirable, but not essential.