Neurobiology Seminar: Scott Linderman: State Space Models for Neural and Behavioral Data

Abstract: New 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 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 ways in which we have used these techniques to gain new insight into the neural computations underlying naturalistic behavior. For example, we have used SSMs to study how neural attractor dynamics encode persistent internal states during social interaction, and to connect stereotyped movements to moment-to-moment fluctuations in dopamine. Together, these projects showcase how our advances in statistics and machine learning offer powerful new tools for linking brain activity and behavior. Biosketch: Scott Linderman PhD is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute, and the Co-Director of the Stanford Center for Neural Data Science. His research focuses on 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 at Columbia University and a graduate student at Harvard University. His work has been recognized with a Savage Award from the International Society for Bayesian Analysis, an AISTATS Best Paper Award, an NSF CAREER Award, and fellowships from the McKnight, Sloan, and Simons Foundations. Publications: Hu, A., Zoltowski, D., Nair, A., Anderson, D., Duncker, L., & Linderman, S. W. (20