Evidence map›Paper›PMID 41799785›Full record

Article... International Conference on Learning Representations2025

TopoNets: High performing vision and language models with brain-like topography.

Mayukh Deb, Mainak Deb, N Apurva Ratan Murty

Abstract read
In one paragraph

Article in ... International Conference on Learning Representations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Beyond binding: from modular to natural vision.Trends in cognitive sciences · 2025
    Review
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

3 authors.

Mayukh DebCognition and Brain Science, School of Psychology, Georgia Tech.
Mainak DebIndependent Contributor.
N Apurva Ratan MurtyCognition and Brain Science, School of Psychology, Georgia Tech.

Funding

Towards a computationally precise characterization of the human ventral visual pathwayR00EY032603 · NEI · GEORGIA INSTITUTE OF TECHNOLOGY · PI N Apurva Ratan Murty · 2024 to 2026
$746k
NEI NIH HHS R00 EY032603
6 · The paper itself

Abstract

Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In this work, we present

Identifiers

PMID41799785
PMCPMC12964247

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.