Journal
NEURON
Volume 98, Issue 6, Pages 1099-+Publisher
CELL PRESS
DOI: 10.1016/j.neuron.2018.05.015
Keywords
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Categories
Funding
- Department of Energy Computational Science Graduate Fellowship program
- Stanford Graduate Fellowship in Science Engineering
- National Science Foundation Graduate Research Fellowship
- NIH F31 Ruth L. Kirschstein National Research Service Award [1F31NS103409-01]
- NSF Graduate Research Fellowship
- Ric Weiland Stanford Graduate Fellowship
- NIH National Institute of Neurological Disorders and Stroke (NINDS) Transformative Research Award [R01NS076460]
- NIH National Institute of Mental Health Grant (NIMH) Transformative Research Award [R01MH09964703]
- NIH Director's Pioneer Award [8DP1HD075623]
- Defense Advanced Research Projects Agency (DARPA) Biological Technology Office (BTO) REPAIR'' award [N66001-10-C-2010]
- DARPA BTO NeuroFAST'' award [W911NF-14-2-0013]
- Simons Foundation Collaboration on the Global Brain awards [325380, 543045]
- Howard Hughes Medical Institute
- NIH [1R21NS104833-01]
- National Science Foundation [1707261]
- U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]
- Burroughs Wellcome Foundation
- McKnight Foundation
- James S. McDonnell Foundation
- Simons Foundation
- Office of Naval Research
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1707261] Funding Source: National Science Foundation
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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 multi-electrode recordings of macaque motor cortex during brain machine interface learning.
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