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Our upcoming event for the Statistics Department Colloquium Series is scheduled for Monday, November 27 from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center Rm. 316. Lunch will be provided to guests following the talk. This week's speaker will be Scott Linderman of the Statistics Department at Stanford University.
Title: Latent States of Brains and Behavior
Abstract: Novel recording technologies are revolutionizing neuroscience, allowing us to measure the spiking activity of hundreds to thousands of neurons in freely behaving animals. These technologies offer exciting opportunities to link brain activity to behavioral output, but they also pose serious statistical challenges. Neural and behavioral data are noisy, high-dimensional time series with nonlinear dynamics and substantial variability across subjects. I will present our work on state space models (SSMs) for such data. The key idea is that these high-dimensional measurements reflect the evolution of low-dimensional latent states, which shed light on how neural circuits compute and how natural behavior is structured. I will present our work on SSMs that disentangle discrete and continuous factors of variation in time series data, and I will highlight several collaborations in which we have used these techniques to gain new insight into the neural computations underlying naturalistic behavior. For example, we have worked with neuroscientists to study how attractor dynamics encode persistent internal states during social interaction, to connect moment-to-moment fluctuations in dopamine to natural behavior, and to develop models of aging with continuous, whole-lifespan behavioral recordings. Together, these projects demonstrate how our contributions to machine learning and statistics offer powerful new tools for linking brain activity and behavior.
Bio: Scott Linderman, PhD, is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute. His research focuses on statistical machine learning, computational neuroscience, and the general question of how computational and statistical methods can help to decipher neural computation. His work combines novel methodological development in the areas of state space models, deep generative models, point processes, and approximate Bayesian inference with applied statistical analyses of large-scale neural and behavioral data. Previously, he was a postdoctoral fellow with David Blei and Liam Paninski at Columbia University and a graduate student at Harvard University with Ryan Adams. His work has been recognized with a Savage Award from the International Society for Bayesian Analysis, an AISTATS Best Paper Award, and fellowships from the McKnight, Sloan, and Simons Foundations.