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

Journal Article · · Neuron
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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

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
NA-0003525; AC04-94AL85000
OSTI ID:
1529675
Alternate ID(s):
OSTI ID: 1477309; OSTI ID: 1495311
Report Number(s):
SAND2018-10605J; S0896627318303878; PII: S0896627318303878
Journal Information:
Neuron, Journal Name: Neuron Vol. 98 Journal Issue: 6; ISSN 0896-6273
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 102 works
Citation information provided by
Web of Science

Cited By (4)

Decoding mood journal October 2018
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

Figures / Tables (13)

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Figure 5, Supplement 2(p. 37)figure Figure 5, Supplement 2
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60 APPLIED LIFE SCIENCES