Evidence map›Paper›PMID 42392073›Full record

ArticleNeuron2026

Sparse component analysis: A method that uncovers separable computations within neural population activity.

Andrew J Zimnik, Xinyue An, K Cora Ames, Andrew Ulmer, Antonio H Lara, Abigail A Russo, Vladislav Susoy, Laura Driscoll, John P Cunningham, Liam Paninski and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuron, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Andrew J ZimnikDepartment of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA.
Xinyue AnDepartment of Neurology, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA.
K Cora AmesDepartment of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
Andrew UlmerDepartment of Neurology, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA.
Antonio H LaraDepartment of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA.
Abigail A RussoDepartment of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA.
Vladislav SusoyDepartment of Physics, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA.
Laura DriscollDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA; Allen Institute for Neural Dynamics, Allen Institute, Seattle, WA, USA; Department of Neurobiology and Biophysics, University of Washington, Seattle, WA, USA.
John P CunninghamZuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Department of Statistics, Columbia University, New York, NY, USA.
Liam PaninskiZuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Department of Statistics, Columbia University, New York, NY, USA.
Mark M ChurchlandDepartment of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA. Electronic address: mc3502@columbia.edu.
Joshua I GlaserDepartment of Neurology, Northwestern University, Chicago, IL, USA; Department of Computer Science, Northwestern University, Evanston, IL, USA; National Institute for Theory and Mathematics in Biology, Chicago, IL, USA. Electronic address: j-glaser@northwestern.edu.

Funding

Modeling multi-area dynamics during motor controlU19NS104649 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI COSTA, RUI M. · 2017 to 2021
$15.3M
Training Program in Neurobiology of Information StorageT32MH067564 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Daniel A Dombeck · 2003 to 2026
$4.7M
Neural representation of mating partners by male C. elegansR01NS113119 · NINDS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI LINDERMAN, SCOTT WARREN, SAMUEL, ARAVINTHAN D. · 2019 to 2023
$3.3M
A dynamical systems approach to fundamental questions in neuroscienceDP2NS083037 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CHURCHLAND, MARK M · 2012 to 2014
$2.6M
Determining the Sources of Motor-Cortex Response ComplexityR01NS135240 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Mark M Churchland · 2024 to 2026
$1.9M
CRCNS: Understanding flexible neural computations in the motor cortexR01NS100066 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CUNNINGHAM, JOHN PATRICK · 2016 to 2018
$937k
Interpretable machine learning for understanding the neural control of movementR00NS119787 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GLASER, JOSHUA I · 2023 to 2025
$673k
NIMH NIH HHS T32 MH067564NINDS NIH HHS DP2 NS083037NINDS NIH HHS R00 NS119787NINDS NIH HHS R01 NS100066NINDS NIH HHS R01 NS113119NINDS NIH HHS R01 NS135240NINDS NIH HHS U19 NS104649
6 · The paper itself

Abstract

In many neural populations, the computationally relevant signals are posited to be a set of "latent factors"-signals shared across many individual neurons. A given brain area may perform many computations, each associated with distinct factors, which can together compose an overall action. The methods for uncovering such structure typically require supervision, which can limit the discovery of novel aspects of activity. Here, we introduce sparse component analysis (SCA), an unsupervised approach. SCA facilitated surprisingly clear parcellations of neural activity across a range of behaviors, when seeking both linear and nonlinear embeddings. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA revealed both simple and unexpected instances where the overall population response was built compositionally from sets of factors with distinct computational roles.

Indexed as

dimensionality reductionlatent factorspopulation activity

Identifiers

PMID42392073
PMCPMC13380302

What OpenQuestion holds

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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.