brAIn Seminar - Jonathan Pillow

— 4:30pm

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

Speaker:
JONATHAN PILLOW, Professor, Princeton Neuroscience Institute, Princeton University
https://pillowlab.princeton.edu/

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types, which limits their ability to capture the functional roles of distinct cell classes or predict the effects of cell-specific perturbations. To overcome these limitations, we introduce the “cell-type dynamical systems” (CTDS) model, which extends latent linear dynamical systems to contain distinct latent variables for each cell class, with appropriate sign constraints on the interactions between them.

In this talk, I will describe the CTDS model and show that fitting in the noiseless case can be reduced to non-negative matrix factorization.  I will then show an application of a multi-region model CTDS to simultaneous recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task.  Remarkably, the model — fit only to unperturbed neural activity — predicts the time-dependent effects of different optogenetic perturbations on behavior, specifically in FOF, ADS, and FOF-to-ADS axon terminals. I will close by discussing the future directions and other applications for biologically-constrained dynamical models of neural activity and behavior. 

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Jonathan Pillow completed his undergraduate education at the University of Arizona in Tucson, where he studied mathematics and philosophy. He received a Ph.D. in neuroscience from New York University in 2005, and was postdoctoral fellow at the Gatsby Computational Neuroscience Unit at University College London. In 2009, he became an assistant professor at the University of Texas at Austin, and in 2014 Jonathan moved to Princeton University to join the Princeton Neuroscience Institute, Psychology department, and Center for Statistics & Machine Learning. Jonathan's current research sits at the border between neuroscience and statistical machine learning, and focuses on computational and statistical methods for understanding how large populations of neurons transmit and process information.  Additional Information. 

In Person Group Viewing and Zoom Participation.  See announcement.

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

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