Brain-computer interfaces (BCIs) are communication and control systems that can provide a direct link between brain responses, behavioral states, actions, and devices. Efficient BCIs require sophisticated computational algorithms and a fundamental understanding of the mediating neural mechanisms.

Computational cognitive neuroscience integrates formal computational modeling techniques with approaches for understanding the neural mechanisms of human cognition (e.g., fMRI, EEG, TMS).

Computational neuroscience applies modeling techniques that are biologically plausible formulations of neuronal function. This approach can be used to test specific hypotheses about the full range of brain function from membrane dynamics to large-scale cortical systems.

Computational vision is an approach that is focused on understanding and creating models of visual system function. It is interdisciplinary and includes research is that is fundamental and applied.

Complex behaviors, including decision making and motor sequences are controlled by sensory input, the internal state, past experience and social interactions. Research in this area exploit model organisms from flies to mice to unravel the mechanisms underlying animal behavior.


Human brain function can be characterized by localized properties (e.g., single cells or regions) as well as large-scale interregional circuits and networks. The fields of computer science, engineering and physics have a long history of developing approaches that allow one to characterize networks and complexity across such scales.

Brain function can be characterized by a variety of noisy signals that vary in their spatial and temporal scales. Research in this theme is focused on how to best extract these signals and identify those that are most meaningful for testing hypothesis about brain structure, function, and behavior.