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

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:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1529675
Alternate Identifier(s):
OSTI ID: 1477309; OSTI ID: 1495311
Report Number(s):
SAND2018-10605J
Journal ID: ISSN 0896-6273; S0896627318303878; PII: S0896627318303878
Grant/Contract Number:  
NA-0003525; AC04-94AL85000
Resource Type:
Published Article
Journal Name:
Neuron
Additional Journal Information:
Journal Name: Neuron Journal Volume: 98 Journal Issue: 6; Journal ID: ISSN 0896-6273
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES

Citation Formats

Williams, Alex H., Kim, Tony Hyun, Wang, Forea, Vyas, Saurabh, Ryu, Stephen I., Shenoy, Krishna V., Schnitzer, Mark, Kolda, Tamara G., and Ganguli, Surya. Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. United States: N. p., 2018. Web. doi:10.1016/j.neuron.2018.05.015.
Williams, Alex H., Kim, Tony Hyun, Wang, Forea, Vyas, Saurabh, Ryu, Stephen I., Shenoy, Krishna V., Schnitzer, Mark, Kolda, Tamara G., & Ganguli, Surya. Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. United States. doi:10.1016/j.neuron.2018.05.015.
Williams, Alex H., Kim, Tony Hyun, Wang, Forea, Vyas, Saurabh, Ryu, Stephen I., Shenoy, Krishna V., Schnitzer, Mark, Kolda, Tamara G., and Ganguli, Surya. Fri . "Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis". United States. doi:10.1016/j.neuron.2018.05.015.
@article{osti_1529675,
title = {Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis},
author = {Williams, Alex H. and Kim, Tony Hyun and Wang, Forea and Vyas, Saurabh and Ryu, Stephen I. and Shenoy, Krishna V. and Schnitzer, Mark and Kolda, Tamara G. and Ganguli, Surya},
abstractNote = {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},
doi = {10.1016/j.neuron.2018.05.015},
journal = {Neuron},
number = 6,
volume = 98,
place = {United States},
year = {2018},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.neuron.2018.05.015

Citation Metrics:
Cited by: 16 works
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