Daniel Levenstein, PhD

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 internally-generated activity improve brain function? This is a bio-computational problem – it requires connecting the emergent organization of neural activity during sleep with the operations it performs on the brain's information processing capacities. Given sleep’s importance for learning and disruption in nearly all neuropsychiatric disorders, solving this mystery is a critical challenge in basic neuroscience with wide-reaching implications for human health and bio-mimetic computing. My lab aims to tackle this problem by building artificial intelligence systems that mimic spontaneous activity in the brain and its use for offline learning.

The work in my lab centers around three questions, using hippocampal replay and its communication with the neocortex as a case study for offline learning: “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?”. To answer these questions, we use artificial neural network (ANN) models, dynamical systems theory, and neural data analysis – working closely with experimental collaborators to inspire the design of our models and to ground 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 cognitive and behavioral implications.