TRACKING THE TOPOLOGY OF NEURAL MANIFOLDS ACROSS POPULATIONS.

Iris H R Yoon, Gregory Henselman-Petrusek, Yiyi Yu, Robert Ghrist, Spencer Lavere Smith, Chad Giusti

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Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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0citing 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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

Updated by
Tracking the topology of neural manifolds across populations.2024

5 · Who and what money

Authors and funding

6 authors.

Iris H R YoonDepartment of Mathematics and Computer Science, Wesleyan University, 265 Church Street Middletown, CT 06459.

Gregory Henselman-PetrusekPacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352.

Yiyi YuDepartment of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA 93106.

Robert GhristDepartment of Mathematics and Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104.

Spencer Lavere SmithDepartment of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA 93106.

Chad GiustiDepartment of Mathematics, Oregon State University, 2000 SW Campus Way, Corvallis, Oregon 97331.

Funding

Visual representations across cortical areasR01EY035378 · NEI · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI SPENCER LAVERE SMITH · 2023 to 2026

$1.6M

NEI NIH HHS R01 EY035378

6 · The paper itself

Abstract

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and

Identifiers

PMID40196150

PMCPMC11975052

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LicenceCC BY-NC-ND

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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.