Evidence map›Paper›PMID 41412130›Full record

ArticleNeuron2026

Synchrony timescales underlie irregular neocortical spiking.

Jagruti J Pattadkal, Ronan T O'Shea, David Hansel, Thibaud Taillefumier, Darrin H Brager, Nicholas J Priebe

Abstract read
In one paragraph

Article in Neuron, 2026. 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. The fluctuation-based regime of thalamocortical circuitry.bioRxiv : the preprint server for biology · 2025
    Article
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  3. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Jagruti J PattadkalDepartment of Neuroscience, The University of Texas at Austin, Austin, TX, USA; Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA. Electronic address: jagruti@austin.utexas.edu.
Ronan T O'SheaDepartment of Neuroscience, The University of Texas at Austin, Austin, TX, USA.
David HanselCerebral Dynamics, Plasticity and Learning Laboratory, CNRS, University Paris Cité, Paris, France.
Thibaud TaillefumierDepartment of Neuroscience, The University of Texas at Austin, Austin, TX, USA; Department of Mathematics, The University of Texas at Austin, Austin, TX, USA.
Darrin H BragerDepartment of Life Sciences, The University of Nevada, Las Vegas, Las Vegas, NV, USA.
Nicholas J PriebeDepartment of Neuroscience, The University of Texas at Austin, Austin, TX, USA; Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA. Electronic address: nicholas@mail.utexas.edu.

Funding

Excitation and inhibition in visual processingR01EY025102 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI PRIEBE, NICHOLAS J · 2014 to 2023
$3.1M
Investigating mechanisms underlying impaired social and spatial cognition in rodent models of Fragile X syndromeR01MH131317 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI BRAGER, DARRIN H, COLGIN, LAURA L · 2022 to 2025
$3.0M
CPS Training ProgramT32EY021462 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI Mary M Hayhoe, Alexander C Huk · 2012 to 2026
$2.5M
CRCNS: Integrating sensory and prior information to control behaviorR01NS120562 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI PRIEBE, NICHOLAS J · 2020 to 2023
$1.1M
NEI NIH HHS R01 EY025102NEI NIH HHS T32 EY021462NIMH NIH HHS R01 MH131317NINDS NIH HHS R01 NS120562
6 · The paper itself

Abstract

Cortical neurons are characterized by their variable spiking patterns. Here, we examine the specific hypothesis that cortical synchrony drives spiking variability in vivo. Using dynamic clamps, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology, we quantify the degree of synchrony and its timescale in cortical networks in vivo. The timescale of synchrony shifts in a range from 25 to 200 ms, depending on the presence of external sensory input. In particular, when the network moves from spontaneous to driven modes, the synchrony timescales shift from slow to fast, leading to a natural reduction in response variability across cortical areas. Finally, while an individual neuron exhibits reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

Indexed as

Action PotentialsNeocortexNerve NetNeuronsAnimalsModels, NeurologicalTime Factorsdynamic clampFano factorneuropixels recordingssynchronyvariabilitywhole-cell recordings

Identifiers

PMID41412130
PMCPMC12768470

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.