Speaker: Scott W. Linderman, Assistant Professor of Statistics, Stanford University
Title: Recurrent State Space Models for Neural and Behavioral Analysis
Abstract: The trend in neural recording capabilities is clear: we can record orders of magnitude more neurons now than we could only a few years ago, and technological advances do not seem to be slowing. Coupled with rich behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. But they also pose serious modeling and algorithmic challenges. We need flexible and interpretable probabilistic models to gain insight from these heterogeneous data and algorithms to efficiently and reliably fit them. I will present some recent work on recurrent switching linear dynamical systems (rSLDS) — models that couple discrete and continuous latent states to model nonlinear processes. I will discuss some approximate Bayesian inference algorithms we've developed to fit these models and infer their latent states, and I will show how these methods can help us gain insight into complex spatiotemporal datasets like those we study in neuroscience.
Speaker's webpage (links to Stanford)
Seminar Date/Time: Thursday April 23rd, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Shizhe Chen (firstname.lastname@example.org) or Pete Scully (email@example.com) for the meeting ID and password, stating your affiliation.