Evidence map›Paper›PMID 28334612›Full record

ArticleNeuron2017

Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions.

Warasinee Chaisangmongkon, Sruthi K Swaminathan, David J Freedman, Xiao-Jing Wang

Abstract read
In one paragraph

Article in Neuron, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing 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

85 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Understanding human metacontrol and its pathologies using deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Linking neural population formatting to function.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. Dynamic tuning of neural stability for cognitive control.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

25 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.

Warasinee ChaisangmongkonDepartment of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA; Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand.
Sruthi K SwaminathanDepartment of Neurobiology, The University of Chicago, Chicago, IL 60637, USA.
David J FreedmanDepartment of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, Chicago, IL 60637, USA.
Xiao-Jing WangDepartment of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA; Center for Neural Science, New York University, New York, NY 10003, USA; NYU-ECNU Joint Institute of Brain and Cognitive Science, NYU-Shanghai, Shanghai 200122, China. Electronic address: xjwang@nyu.edu.

Funding

Cortical Mechanisms of Visual Category Recognition and LearningR01EY019041 · NEI · UNIVERSITY OF CHICAGO · PI David J Freedman · 2009 to 2026
$8.5M
Recurrent Neual Circuit Basis of Time Integration and Decision MakingR01MH062349 · NIMH · YALE UNIVERSITY · PI WANG, XIAO-JING · 2001 to 2022
$7.2M
CRCNS: Uncovering neurla circuit mechanisms of category computation and learningR01MH092927 · NIMH · YALE UNIVERSITY · PI FREEDMAN, DAVID J, WANG, XIAO-JING · 2010 to 2014
$1.6M
NEI NIH HHS R01 EY019041NIMH NIH HHS R01 MH062349NIMH NIH HHS R01 MH092927
6 · The paper itself

Abstract

Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a "neural landscape" consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.

Indexed as

Neural Networks, ComputerAnimalsDecision MakingMacaca mulattaMaleModels, NeurologicalNeuronsParietal LobePrefrontal Cortexcategory learningdecision makingdelayed match-to-category taskhessian-free algorithmlateral intraparietal cortexLIPPFCprefrontal cortexrecurrent neural networkworking memory

Identifiers

PMID28334612
PMCPMC5586485

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.