Evidence map›Paper›PMID 41728266›Full record

ArticleFrontiers in computational neuroscience2025

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

Motolani Olarinre, Joshua H Siegle, Robert E Kass

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Motolani OlarinreMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA, United States.
Joshua H SiegleAllen Institute for Neural Dynamics, Seattle, WA, United States.
Robert E KassMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA, United States.

Funding

Diversity supplement to Analysis of Nonstationary Point Process DataR01MH064537 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI KASS, ROBERT E · 2001 to 2024
$7.2M
NIMH NIH HHS R01 MH064537
6 · The paper itself

Abstract

Introduction: The onset of a sensory stimulus elicits transient bursts of activity in neural populations, which are presumed to convey information about the stimulus to downstream populations. Although the timing at which these synchronized bursts reach their peak is highly variable across stimulus presentations, the relative timing of bursts across interconnected brain regions may be less variable, particularly for regions that are strongly functionally coupled. Methods: We developed a simple analytical framework that provides accurate trial-by-trial estimates of population burst times and of the correlations in the timing of evoked population bursts across areas. The method was evaluated using simulated data and compared to a recently published alternative model. We then applied the approach to large-scale Neuropixels recordings from six cortical visual areas and one visual thalamic nucleus in thirteen mice presented with drifting grating stimuli. Results: Our method performed well on simulated data and was 85-90% faster than the alternative model while being substantially easier to apply. Applied to real data, the approach enabled identification of mouse-to-mouse variation in both peak times and region-to-region functional coupling for the first two population bursts following stimulus onset. The observed timing relationships were consistent with known anatomy and physiology. Discussion: Examining sequences of activity across areas revealed that some timing relationships were preserved across all mice, while others varied across individuals. These findings demonstrate that the general approach can produce sensitive, trial-resolved analyses of timing relationships across neural populations and can capture both shared and individual-specific patterns of population burst propagation.

Indexed as

Bayesian denoisingcross-population interactionneuropixelsspike train denoisingvisual cortex

Identifiers

PMID41728266
PMCPMC12920600

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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.