Theoretical analysis and computational modeling of network dynamics, memory storage and recall, spike-timing dependent synaptic plasticity, and multi-timescale adaptation in neural systems.

Theoretical and computational methods investigating the circuitry of the cerebral cortex, the rules by which this circuitry develops or "self-organizes", and the computational functions of this circuitry.

Machine learning and statistics for understanding computation in neural systems, in particularly recorded activity of large neural populations.

The statistical and theoretical analysis of neural networks that produce multiple activity states, their relationship to behavior and cognition, and their disruption in neural pathologies.

The neural basis and computational aspects of rule representation in the brain.

Statistical analysis at various levels of the neural code, from ensembles of simultaneously-recorded spike trains down to voltage fluctuations in individual dendritic compartments.

Computational modeling of stereovision, motion processing, motion-stereo integration, orientation plasticity, and sensorimotor integration; Visual psychophysics.

Models of brain function.

Nonlinear signaling, microscopic and mesoscopic dynamics in neural networks, emergence of large-scale phenomena such as control and learning of behavior.

Information processing in cells, robustness of biological switches, synthetic biology, determination of cell shape, computational analysis of gene transcription networks in cancer, computational analysis of metabolic networks in bacteria.