Evidence map›Paper›PMID 32640928›Full record

ReviewAnnual review of neuroscience2020

Computation Through Neural Population Dynamics.

Saurabh Vyas, Matthew D Golub, David Sussillo, Krishna V Shenoy

Abstract readReview
In one paragraph

Review in Annual review of neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 356 papers.

0numbers the graph read from it
0cells of the map it votes in
356citing 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

356 citing papers in PubMed.

  1. Article
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  3. Review
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  9. Review
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  14. Feasible cognition requires latent state.Biological cybernetics · 2026
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

296 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

4 authors.

Saurabh VyasDepartment of Bioengineering, Stanford University, Stanford, California 94305, USA; email: smvyas@stanford.edu.
Matthew D GolubDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
David SussilloDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
Krishna V ShenoyDepartment of Bioengineering, Stanford University, Stanford, California 94305, USA; email: smvyas@stanford.edu.

Funding

Engaging new cognitive and motor signals to improve communication prosthesesR01DC014034 · NIDCD · STANFORD UNIVERSITY · PI JAIMIE M HENDERSON · 2015 to 2026
$8.2M
Understanding the Neural Basis of Volitional State through Continuous Recordings in HumansU01NS098968 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2016 to 2018
$4.7M
Intuitive, complete neural control of tablet computers for communicationU01DC017844 · NIDCD · BROWN UNIVERSITY · PI HOCHBERG, LEIGH R · 2019 to 2025
$4.3M
High-Bandwidth Wireless Interfaces for Continuous Human Intracortical RecordingUH2NS095548 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI HOCHBERG, LEIGH R, NURMIKKO, ARTO · 2015 to 2019
$3.7M
Neural population dynamics in premotor cortex during decision makingK99MH121533 · NIMH · STANFORD UNIVERSITY · PI GOLUB, MATTHEW D. · 2019 to 2021
$282k
Neural dynamics and adaption for brain machine interface controlF31NS103409 · NINDS · STANFORD UNIVERSITY · PI VYAS, SAURABH · 2017 to 2019
$102k
NIDCD NIH HHS R01 DC014034NIDCD NIH HHS U01 DC017844NIMH NIH HHS K99 MH121533NINDS NIH HHS F31 NS103409NINDS NIH HHS U01 NS098968NINDS NIH HHS UH2 NS095548
6 · The paper itself

Abstract

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Indexed as

Computational BiologyDeep LearningAnimalsBrainHumansNerve NetNeuronsPopulation Dynamicsdynamical systemsneural computationneural population dynamicsstate spaces

Identifiers

PMID32640928
PMCPMC7402639

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.