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

Journal: Neuron
PMID: 29887338

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.

Authors

  • Alex H Williams
    Department of Neurosciences, University of California, San Diego, La Jolla, United States.
  • Tony Hyun Kim
    Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.
  • Forea Wang
    Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA.
  • Saurabh Vyas
    Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA.
  • Stephen I Ryu
  • Krishna V Shenoy
    1] Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA. [2] Departments of Bioengineering and Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California, USA.
  • Mark Schnitzer
    Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Biology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94305, USA.
  • Tamara G Kolda
    Sandia National Laboratories, Livermore, CA 94551, USA.
  • Surya Ganguli
    Department of Applied Physics, Stanford University, Stanford, CA 94305, United States.

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