We're looking for exceptional postdocs and grad students to work in a highly collaborative and interdisciplinary group.
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Our goal is to identify efficient strategies that animals and brains use to make sense of their surroundings and guide smart behavior.
If only our brains had unlimited computational resources, we might use a complete knowledge of the environment and the possible consequences of our actions to behave in near-optimal ways. But real brains use components that operate under energetic and computational constraints, and real bodies only permit biomechanically appropriate actions. One way that brains solve this problem is by flexibly linking sensation and action through abstract internal representations that preserve behaviorally relevant information about statistical relationships in the environment and expected outcomes of actions. Our goal is to understand the principles by which such representations are built, modified, and used to achieve desirable behavioral goals in the face of uncertainty and change.
We approach these problems from two complementary perspectives: we take a theory-driven approach to bridging the gap between normative theories of sensory coding, inference, and action selection, and we take a data-driven approach to studying the circuit implementation of sensorimotor representations that support flexible behavior.