Evidence map›Paper›PMID 29887338›Full record

ArticleNeuron2018

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

Alex H Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I Ryu, Krishna V Shenoy, Mark Schnitzer, Tamara G Kolda, Surya Ganguli

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuron, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers.

0numbers the graph read from it
0cells of the map it votes in
154citing papers in PubMed
16.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

154 citing papers in PubMed, 366 citations in OpenAlex.

  1. Article
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  6. Transformations of the spatial activity manifold convey aversive information in CA3.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  8. Decomposing the modulation of interactions between neuronal populations.bioRxiv : the preprint server for biology · 2026
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  17. Neural substrate of conditioned stimulus for associative learning in the hippocampus.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  18. Review
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  20. Article

94 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Alex H WilliamsNeurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA. Electronic address: ahwillia@stanford.edu.
Tony Hyun KimElectrical Engineering Department, Stanford University, Stanford, CA 94305, USA.
Forea WangNeurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA.
Saurabh VyasElectrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA.
Stephen I RyuElectrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA.
Krishna V ShenoyElectrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
Mark SchnitzerApplied Physics Department, Stanford University, Stanford, CA 94305, USA; Biology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94305, USA.
Tamara G KoldaSandia National Laboratories, Livermore, CA 94551, USA.
Surya GanguliApplied Physics Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: sganguli@stanford.edu.
Stanford University · USHoward Hughes Medical Institute · USPalo Alto Institute · USSandia National Laboratories California · US

Funding

Fully Implantable and Programmable Spike-based Codecs for NeuroprostheticsR01NS076460 · NINDS · STANFORD UNIVERSITY · PI BOAHEN, KWABENA · 2011 to 2015
$4.4M
Toward an Animal Model of Freely Moving HumanDP1HD075623 · NICHD · STANFORD UNIVERSITY · PI SHENOY, KRISHNA V · 2012 to 2013
$1.6M
Ensemble neural dynamics in the medial prefrontal cortex underlying cognitive flexibility and reinforcement learningR21NS104833 · NINDS · STANFORD UNIVERSITY · PI GANGULI, SURYA, SCHNITZER, MARK J · 2017 to 2018
$436k
Neural dynamics and adaption for brain machine interface controlF31NS103409 · NINDS · STANFORD UNIVERSITY · PI VYAS, SAURABH · 2017 to 2019
$102k
Howard Hughes Medical InstituteNICHD NIH HHS DP1 HD075623NINDS NIH HHS F31 NS103409NINDS NIH HHS R01 NS076460NINDS NIH HHS R21 NS104833
6 · The paper itself

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.

Indexed as

Brain-Computer InterfacesNeural Networks, ComputerUnsupervised Machine LearningAnimalsMacaca mulattaMiceMotor CortexPrefrontal CortexPrincipal Component AnalysisSpatial NavigationTime Factorsbrain machine interfacesdimensionality reductiongain modulationlarge-scale recordingslearningmotor controlnavigationneural data analysisrecurrent neural networkssingle-trial analysis

Identifiers

PMID29887338
PMCPMC6907734
OpenAlexW2951881727

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.