Exactly solvable statistical physics models for large neuronal populations.

Christopher W Lynn, Qiwei Yu, Rich Pang, William Bialek, Stephanie E Palmer

Abstract readPreprint

In one paragraph

Article in ArXiv, 2023. 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

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Exactly solvable statistical physics models for large neuronal populations.

5 · Who and what money

Authors and funding

5 authors.

Christopher W LynnInitiative for the Theoretical Sciences, The Graduate Center, City University of New York, New York, NY 10016, USA.

Qiwei YuLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Rich PangPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.

William BialekJoseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA.

Stephanie E PalmerDepartment of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, USA.

Funding

Coarse-graining approaches to networks, learning, and behaviorR01EB026943 · NIBIB · UNIVERSITY OF CHICAGO · PI BIALEK, WILLIAM, PALMER, STEPHANIE E · 2018 to 2020

$1.1M

NIBIB NIH HHS R01 EB026943

6 · The paper itself

Abstract

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of

Identifiers

PMID37904743

PMCPMC10614989

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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.