Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Alex H. Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I. Ryu, Krishna V. Shenoy, Mark Schnitzer, Tamara G. Kolda, Surya Ganguli
doi: https://doi.org/10.1101/211128
Alex H. Williams
1Neurosciences Graduate Program, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tony Hyun Kim
2Electrical Engineering Department, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Forea Wang
1Neurosciences Graduate Program, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saurabh Vyas
2Electrical Engineering Department, Stanford, CA 94305, USA.
3Bioengineering Department, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stephen I. Ryu
2Electrical Engineering Department, Stanford, CA 94305, USA.
11Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Krishna V. Shenoy
2Electrical Engineering Department, Stanford, CA 94305, USA.
3Bioengineering Department, Stanford, CA 94305, USA.
6Neurobiology Department, Stanford, CA 94305, USA.
7Bio-X Program, Stanford, CA 94305, USA.
8Stanford Neurosciences Institute, Stanford, CA 94305, USA.
9Howard Hughes Medical Institute, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark Schnitzer
4Applied Physics Department, Stanford, CA 94305, USA.
5Biology Department, Stanford, CA 94305, USA.
7Bio-X Program, Stanford, CA 94305, USA.
9Howard Hughes Medical Institute, Stanford, CA 94305, USA.
10, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tamara G. Kolda
12Sandia National Laboratories, Livermore, CA 94551, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Surya Ganguli
4Applied Physics Department, Stanford, CA 94305, USA.
6Neurobiology Department, Stanford, CA 94305, USA.
7Bio-X Program, Stanford, CA 94305, USA.
8Stanford Neurosciences Institute, Stanford, CA 94305, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 components 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted October 30, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
Alex H. Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I. Ryu, Krishna V. Shenoy, Mark Schnitzer, Tamara G. Kolda, Surya Ganguli
bioRxiv 211128; doi: https://doi.org/10.1101/211128
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
Alex H. Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I. Ryu, Krishna V. Shenoy, Mark Schnitzer, Tamara G. Kolda, Surya Ganguli
bioRxiv 211128; doi: https://doi.org/10.1101/211128

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3686)
  • Biochemistry (7780)
  • Bioengineering (5671)
  • Bioinformatics (21250)
  • Biophysics (10565)
  • Cancer Biology (8164)
  • Cell Biology (11915)
  • Clinical Trials (138)
  • Developmental Biology (6740)
  • Ecology (10388)
  • Epidemiology (2065)
  • Evolutionary Biology (13845)
  • Genetics (9695)
  • Genomics (13058)
  • Immunology (8129)
  • Microbiology (19970)
  • Molecular Biology (7839)
  • Neuroscience (42991)
  • Paleontology (318)
  • Pathology (1276)
  • Pharmacology and Toxicology (2257)
  • Physiology (3350)
  • Plant Biology (7208)
  • Scientific Communication and Education (1309)
  • Synthetic Biology (2000)
  • Systems Biology (5529)
  • Zoology (1126)