brAIn Seminar - Scott Linderman

— 4:30pm

Location:
In Person and Virtual - ET - Group Viewing, Baker Hall 340A and Zoom

Speaker:
SCOTT LINDERMAN , Assistant Professor of Statistics, and, Institute Scholar, Wu Tsai Neurosciences Institute , Stanford University
https://web.stanford.edu/~swl1/

State Space Models for Biological and Artificial Intelligence

New recording technologies are revolutionizing neuroscience, allowing us to measure the spiking activity of large numbers 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) to tackle these challenges. The key idea is that high-dimensional measurements often reflect the evolution of underlying latent states, whose dynamics may shed light on neural computation. For example, we have used SSMs to study how attractor dynamics in the hypothalamus encode persistent internal states during social interaction, and to connect stereotyped movements to moment-to-moment fluctuations in brain activity. 

There has been a resurgence of interest in SSMs within the machine learning community as well, and SSMs now form the backbone of several state-of-the-art models for sequential data. I will present recent work from my lab that focuses on novel models and efficient algorithms for sequential data, with applications in neuroscience and beyond. Together, these projects highlight the central role of state space models in our studies of both biological and artificial intelligence. 

— 

Scott Linderman PhD is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute.  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 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. 

In Person/Group Viewing and Zoom Participation.  See announcement.  

Event Website:
https://brain.andrew.cmu.edu/seminar

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