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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)
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. 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., & 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 = {2018},
month = {6}
}

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

Citation Metrics:
Cited by: 27 works
Citation information provided by
Web of Science

Figures / Tables:

Fig 1 Fig 1: Tensor representation of trial-structured neural data. (a) Schematic of trial-averaged PCA for spiking data. The raw data is represented as a sequence of $N$ x $T$ matrices (top). These matrices are averaged across trials to build a matrix representation of neural firing rates. PCA approximates the trial-averaged matrixmore » as a sum of outer products of vectors (see eq. (1)). Each outer product contains a neuron factor (blue rectangles) and a temporal factor (red rectangles). (b) Schematic of trial-concatenated PCA for spiking data. Raw data are temporally smoothed by a Gaussian filter to estimate neural firing rates before concatenating all trials along the time axis. Applying PCA produces a separate set of temporal factors for each trial (subsets of the red vectors). (c) Schematic of TCA. Raw data are smoothed and collected into a third order tensor with dimensions $N$ =x $T$ x $K$. TCA approximates the data as a sum of outer products of three vectors, producing a third set of low-dimensional factors (trial factors, green vectors) that describe how activity changes across trials.« less

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Works referencing / citing this record:

Decoding mood
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Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
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Decoding mood
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Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
journal, July 2019


ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings
journal, February 2018


Lateral orbitofrontal cortex promotes trial-by-trial learning of risky, but not spatial, biases
journal, November 2019

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