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2018-19
Surya Ganguli, PhD

Surya Ganguli, PhD

BME Seminar Series Spring 2019

Thursday, March 4, 2018
Host: Professor Lee Miller

Assistant Professor, Department of Applied Physics, Stanford University

Unsupervised Discovery of Structure in Neural Data Across Multiple Time-Scales: Theory and Application

Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid singletrial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

Learn more about Professor Surya Ganguli and their research.