Like learning, sleep changes the brain to improve its future performance. Unlike learning, these changes occur in the absence of overt behavior or sensory input. This “offline learning” thus contains a mystery: how does spontaneous activity, which is generated by the brain itself, improve brain function? Our lab aims to develop theories of offline learning that shed light on this mystery and can be used to mimic its computational benefits in artificial neural networks or understand its disruption in neuropsychiatric disorders.

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Our work generally focuses on spatial representation in the hippocampal formation, and a phenomenon called sharp wave ripples - high frequency oscillations in the hippocampus that coordinate activity across the brain and simulate wake-like “replay” trajectories during sleep. We use artificial neural networks (ANNs), dynamical systems theory, and neural data analysis to study how these internally-generated dynamics support offline learning – working closely with experimental collaborators to inspire the design of computational models and to compare them in experimental data. This NeuroAI approach, in which brain-inspired ANNs are built and used as models for the brain, is particularly well-suited to bridge neurons’ circuit and cellular-level properties with their computational capacities, and allows us to study three questions central to our research.: “How does spontaneous activity emerge and self-organize in neural networks?”, “How does plasticity during spontaneous activity change the brain?”, and “How do those changes improve the brain’s operations and performance on future tasks?”.

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In addition to our work on sleep, the lab works broadly at the interface of theoretical and experimental neuroscience - applying computational methods to a variety of interesting problems involving neural dynamics and computation with experimentalist collaborators.