One of the fundamental goals of neuroscience is to understand how the organization and the dynamics of the neural circuits underly their ability to drive adaptive behaviour. The cerebellar cortex provides a unique system to define such structure-function relationships due to its accessibility, well-defined architecture, and involvement in pervasive brain disorders as autism. Recent evidence suggests that the cerebellum may generate predictions to facilitate a variety of motor and non-motor behaviours, including cognitive processes. The question is how such a diversity of function emerges from such a homogenous structure. The aim of this project is to characterize the activity of the different cerebellar neurons during a wide range of states to underly their shared and diverging roles in different behavioural paradigms. First, I aim to develop a machine-learning based classifier for identifying cerebellar cell types from Neuropixels recordings. Then, I will use the classifier to characterize the activity of different cerebellar neurons in reward prediction and social interaction tasks. A direct projection from the deep cerebellar nuclei to the ventral tegmental area is believed to modulate both reward circuity and social interaction. However, the cerebellar mechanism underlying these two behaviours is unknown. I will measure and compare the activity of identified classes of cerebellar neurons in these behaviours. Finally, since autism is characterized by having both impaired social behaviour and impaired reward processing, I will also record from autism model mice in the social task. Understanding the computations implemented by the cerebellum in a variety of behavioural states will reveal general principles about how neurons process information and underlie brain function in ‘normal’ vs. pathological conditions.
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