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Explore how machine learning techniques can reverse-engineer neural computation in this research showcase talk from the Paul G. Allen School. Discover how the Systems Neuroscience and AI Lab (SNAIL) develops computational models and analytical tools to understand brain function through large-scale recordings of neural activity from hundreds or thousands of neurons. Learn about viewing neural populations as dynamical systems whose structure and activity evolve over time, and examine how this perspective enables efficient identification of causal structure in biological neural networks through algorithmically chosen external perturbations. Delve into deep learning approaches to dynamical systems modeling that reveal how neural population activity changes during learning and how multiple brain regions communicate to support distributed computation. Understand how these advances illuminate fundamental principles of brain organization for flexible and intelligent behavior, covering applications in movement generation, decision-making, and learning from experience. Gain insights into the intersection of neuroscience, neuroengineering, machine learning, and data science through research that bridges brain-computer interfaces, feedback motor control, and neural population dynamics.
