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)
- 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. https://doi.org/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. https://doi.org/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 = {Fri Jun 01 00:00:00 EDT 2018},
month = {Fri Jun 01 00:00:00 EDT 2018}
}
https://doi.org/10.1016/j.neuron.2018.05.015
Web of Science
Figures / Tables:
Works referencing / citing this record:
Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
journal, July 2019
- Unakafova, Valentina A.; Gail, Alexander
- Frontiers in Neuroinformatics, Vol. 13
Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
journal, July 2019
- Unakafova, Valentina A.; Gail, Alexander
- Frontiers in Neuroinformatics, Vol. 13
ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings
journal, February 2018
- O’Shea, Daniel J.; Shenoy, Krishna V.
- Journal of Neural Engineering, Vol. 15, Issue 2
Lateral orbitofrontal cortex promotes trial-by-trial learning of risky, but not spatial, biases
journal, November 2019
- Constantinople, Christine M.; Piet, Alex T.; Bibawi, Peter
- eLife, Vol. 8
Figures / Tables found in this record: