Computational neuroscience is unique among areas of theoretical and quantitative biology for its emphasis on representation and information processing – more than any other organ, the brain computes. Representations are apparent in sensory systems, where neural responses can be interpretted as encoding information about features of a visual scene, for example. Notions of representation in the motor system are less clear, and it remains an open question whether primary motor cortex is best thought of as encoding information about limb kinetics, kinematics, or some other variables.
Alternatively, perhaps the notion of such explicit encoding is not relevant, and a more dynamical systems-based approach is instead warranted. These issues of representation carry weight in the design of brain-computer interfaces (BCIs), where the successful implementation of sophisticated BCIs will likely rely on being able to properly interpret the neural code. I’m interested in understanding how the brain performs motor control and how that knowledge can be used to build better BCIs.
More generally I’m excited by technologies and datasets driving neuroscience forward. Optical and electrical recording technologies now allow large populations of neurons to be recorded from simultaneously. For instance, optical imaging can now be performed for whole animals, while freely behaving, providing an unprecedented window into the functioning of (lower) organisms. I’m interested in statistical methods for learning dynamical structure in such high-dimensional datasets.
Before my doctoral studies, my interests were in systems biology and bioinformatics.