In order to survive and reproduce, animals must produce a diverse range of innate and learned behaviours in a flexible and context-dependent manner. The computational task of forming an internal representation of an animal’s environment and translating that to the selection of goal-directed actions is dependent on the coordinated activity of multiple brain areas. Working with experimentalists to dissect the neural correlates of behaviour in multiple interconnected hypothalamic nuclei of freely behaving mice, we uncover striking differences in how animals’ motivational states and behaviours are represented across neural populations, informing a new model of the joint control of behavioural decision-making by multiple interacting brain regions. 

Ann Kennedy is a theoretical neuroscientist investigating neural computation and the structure of behaviour. Her newly founded lab at Northwestern develops machine learning tools for automated quantification and analysis of animal behaviour, and applies these tools in collaboration with experimental labs to better characterize the neural correlates of behaviour in multiple brain regions. She was previously a postdoctoral researcher in the David Anderson research group at Caltech, where she characterized the dynamics of hypothalamic circuits that governs social and fear behaviours, and she earned her PhD with Larry Abbott at the Center for Theoretical Neuroscience at Columbia University, studying neural representations and learning in cerebellum-like structures.

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