Evidence map›Paper›PMID 39560916›Full record

ArticleJournal of computational neuroscience2025

Effect of burst spikes on linear and nonlinear signal transmission in spiking neurons.

Maria Schlungbaum, Alexandra Barayeu, Jan Grewe, Jan Benda, Benjamin Lindner

Abstract read
In one paragraph

Article in Journal of 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

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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maria SchlungbaumBernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, 10115, Berlin, Germany. maria.schlungbaum@bccn-berlin.de.
Alexandra BarayeuNeuroethology, Institute for Neurobiology of Eberhard Karls University Tübingen, Auf der Morgenstelle 28, 72076, Tübingen, Germany.
Jan GreweNeuroethology, Institute for Neurobiology of Eberhard Karls University Tübingen, Auf der Morgenstelle 28, 72076, Tübingen, Germany.
Jan BendaNeuroethology, Institute for Neurobiology of Eberhard Karls University Tübingen, Auf der Morgenstelle 28, 72076, Tübingen, Germany.
Benjamin LindnerBernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, 10115, Berlin, Germany.

Funding

Deutsche Forschungsgemeinschaft LI 1046/1, BE3699/1-1, GR2633/5-1
6 · The paper itself

Abstract

We study the impact of bursts on spike statistics and neural signal transmission. We propose a stochastic burst algorithm that is applied to a burst-free spike train and adds a random number of temporally-jittered burst spikes to each spike. This simple algorithm ignores any possible stimulus-dependence of bursting but allows to relate spectra and signal-transmission characteristics of burst-free and burst-endowed spike trains. By averaging over the various statistical ensembles, we find a frequency-dependent factor connecting the linear and also the second-order susceptibility of the spike trains with and without bursts. The relation between spectra is more complicated: besides a frequency-dependent multiplicative factor it also involves an additional frequency-dependent offset. We confirm these relations for the (burst-free) spike trains of a stochastic integrate-and-fire neuron and identify frequency ranges in which the transmission is boosted or diminished by bursting. We then consider bursty spike trains of electroreceptor afferents of weakly electric fish and approach the role of burst spikes as follows. We compare the spectral statistics of the bursty spike train to (i) that of a spike train with burst spikes removed and to (ii) that of the spike train in (i) endowed by bursts according to our algorithm. Significant spectral features are explained by our signal-independent burst algorithm, e.g. the burst-induced boosting of the nonlinear response. A difference is seen in the information transfer for the original bursty spike train and our burst-endowed spike train. Our algorithm is thus helpful to identify different effects of bursting.

Indexed as

Action PotentialsModels, NeurologicalNeuronsNonlinear DynamicsSynaptic TransmissionAlgorithmsAnimalsElectric FishElectric OrganLinear ModelsStochastic ProcessesBurstingElectrosensingNeural information transmissionStochastic neuron model

Identifiers

PMID39560916
PMCPMC11868171

What OpenQuestion holds

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LicenceCC BY
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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.