Evidence map›Paper›PMID 26733859›Full record

ArticleFrontiers in neuroinformatics2015

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.

Jens-Oliver Muthmann, Hayder Amin, Evelyne Sernagor, Alessandro Maccione, Dagmara Panas, Luca Berdondini, Upinder S Bhalla, Matthias H Hennig

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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  9. AECuration: automated event curation for spike sorting.Journal of neural engineering · 2025
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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

8 authors.

Jens-Oliver MuthmannManipal UniversityManipal, India; Department of Neurobiology, National Centre for Biological Sciences, Tata Institute of Fundamental ResearchBangalore, India; School of Informatics, Institute for Adaptive and Neural Computation, University of EdinburghEdinburgh, UK.
Hayder AminDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
Evelyne SernagorInstitute of Neuroscience, Newcastle University Newcastle, UK.
Alessandro MaccioneDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
Dagmara PanasSchool of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.
Luca BerdondiniDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
Upinder S BhallaDepartment of Neurobiology, National Centre for Biological Sciences, Tata Institute of Fundamental Research Bangalore, India.
Matthias H HennigSchool of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/H023569/1Biotechnology and Biological Sciences Research Council BB/H023607/1Biotechnology and Biological Sciences Research Council BB/I001042/1Medical Research Council G0900425
6 · The paper itself

Abstract

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

Indexed as

correlationscultured neuronsmicroelectrode arrayretinaspike detection

Identifiers

PMID26733859
PMCPMC4683190

What OpenQuestion holds

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Registered trials

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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.