Summary statistics of learning link changing neural representations to behavior.

Jacob A Zavatone-Veth, Blake Bordelon, Cengiz Pehlevan

Abstract readPreprint

In one paragraph

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

0cells of the map it votes in

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

Updated by
Summary statistics of learning link changing neural representations to behavior.2025

5 · Who and what money

Authors and funding

3 authors.

Jacob A Zavatone-VethCenter for Brain Science, Harvard University, Cambridge, MA, USA.

Blake BordelonJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Cengiz PehlevanJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Funding

Understanding data-driven models for neural dynamicsDP5OD037354 · OD · HARVARD UNIVERSITY · PI Jacob Andreas Zavatone-Veth · 2024 to 2026

$1.3M

NIH HHS DP5 OD037354

6 · The paper itself

Abstract

How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.

Identifiers

PMID40313661

PMCPMC12045385

What Socratic holds

Textmetadata

LicenceCC BY

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 Socratic graph.