Evidence map›Paper›PMID 41271767›Full record

ArticleNature communications2025

Adaptive stretching of representations across brain regions and deep learning model layers.

Xin-Ya Zhang, Sebastian Bobadilla-Suarez, Xiaoliang Luo, Marilena Lemonari, Scott L Brincat, Markus Siegel, Earl K Miller, Bradley C Love

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026
    Review
  2. Article
  3. Heavy-tailed update distributions arise from information-driven self-organization in nonequilibrium learning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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

8 authors.

Xin-Ya ZhangCenter for Interdisciplinary Studies and Department of Physics, School of Science, Westlake University, Hangzhou, PR China. zhangxinya@westlake.edu.cn.ORCID http://orcid.org/0000-0002-7826-3076
Sebastian Bobadilla-SuarezDepartment of Experimental Psychology, University College London, London, UK.ORCID http://orcid.org/0000-0002-5951-0772
Xiaoliang LuoDepartment of Experimental Psychology, University College London, London, UK.
Marilena LemonariComputer Science, University of Cyprus, Nicosia, Cyprus.ORCID http://orcid.org/0000-0001-9854-3369
Scott L BrincatThe Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-7777-7294
Markus SiegelDepartment of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.ORCID http://orcid.org/0000-0001-5115-936X
Earl K MillerThe Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-0582-6958
Bradley C LoveLos Alamos National Laboratory, Los Alamos, NM, USA. love@lanl.gov.ORCID http://orcid.org/0000-0002-7883-7076

Funding

China Scholarship Council (CSC) 202206260103RCUK | Economic and Social Research Council (ESRC) ES/W007347/1Royal Society 183029United States Department of Defense | United States Navy | ONR | Marine Corps Warfighting Laboratory (MCWL) N00014-23-1-2768Wellcome Trust
6 · The paper itself

Abstract

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model's representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.

Indexed as

BrainDeep LearningPrefrontal CortexAnimalsAttentionMacaca mulattaMaleModels, NeurologicalNeuronsPhotic StimulationVisual Perception

Identifiers

PMID41271767
PMCPMC12638828

What OpenQuestion holds

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Registered trials

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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.