Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis

Alex H. Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I. Ryu, Krishna V. Shenoy, Mark Schnitzer, Tamara G. Kolda, Surya Ganguli

Research output: Contribution to journalArticlepeer-review

Abstract

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 single-trial 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. Williams et al. describe an unsupervised method to uncover simple structure in large-scale recordings by extracting distinct cell assemblies with rapid within-trial dynamics, reflecting interpretable aspects of perceptions, actions, and thoughts, and slower across-trial dynamics reflecting learning and internal state changes.

Original languageEnglish (US)
Pages (from-to)1099-1115.e8
JournalNeuron
Volume98
Issue number6
DOIs
StatePublished - Jun 27 2018

Keywords

  • brain machine interfaces
  • dimensionality reduction
  • gain modulation
  • large-scale recordings
  • learning
  • motor control
  • navigation
  • neural data analysis
  • recurrent neural networks
  • single-trial analysis
  • Brain-Computer Interfaces
  • Neural Networks, Computer
  • Spatial Navigation/physiology
  • Macaca mulatta
  • Motor Cortex/physiology
  • Unsupervised Machine Learning
  • Animals
  • Time Factors
  • Mice
  • Principal Component Analysis
  • Prefrontal Cortex/physiology

ASJC Scopus subject areas

  • Neuroscience(all)

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