I am interested in how the model bias in biological neuronal networks realized through network architecture, neuronal nonlinearities, and their dynamics lead to more robust inference and faster learning. I use deep learning to approach these questions through theoretical analysis and system identification on large scale neuro-physiological and -anatomical data.
My interest lies in how a population of cortical neurons encode and subsequently perform computations on the world state variables. In particular, I have studied how sensory cortical population represent visual stimulus information including the sensory uncertainty in accordance to the theory of probabilistic population coding (PPC), combining population electrophysiology from Macaque V1 with Bayesian and neural network analysis to decode the likelihood functions. I have also been working on building network models of sensory cortex to understand the representation and computation carried out by repeating canonical computation units in mouse brain.