Our broad interest is in understanding how large networks of neurons, e.g. those in the cerebral cortex, process sensory inputs and give rise to our perception and cognitive functions through their collective dynamics and learning on multiple timescales. To shed light on the complexity of neurobiological phenomena we use simplifed mathematical models that capture core mechanistic features of the circuit and connect them to its computational goals. A second line of interest is development of new statistical and computational tools for analyzing large, high-dimensional neural and behavioral datasets. We collaborate with several experimental labs. Some current questions of interest are: How do horizontal and feedback connections in sensory cortical areas serve computations ranging from contextual modulations to dynamic representation of objects? How are the latter related to the balance of excitation and inhibition? How do these computations adapt to changes in stimulus statistics?
