Evidence map›Paper›PMID 31953614›Full record

ArticleJournal of computational neuroscience2020

Fast simulation of extracellular action potential signatures based on a morphological filtering approximation.

Harry Tran, Radu Ranta, Steven Le Cam, Valérie Louis-Dorr

Abstract read
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In one paragraph

Article in Journal of computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Harry TranCNRS, CRAN, Université de Lorraine, F-54000, Nancy, France.
Radu RantaCNRS, CRAN, Université de Lorraine, F-54000, Nancy, France. radu.ranta@univ-lorraine.fr.ORCID 0000-0002-0893-8998
Steven Le CamCNRS, CRAN, Université de Lorraine, F-54000, Nancy, France.
Valérie Louis-DorrCNRS, CRAN, Université de Lorraine, F-54000, Nancy, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations difficult without a workstation. We propose in this paper a novel method to simulate extracellular potentials of firing neurons, taking into account the NEURON geometry and the relative positions of the electrodes. The simulator takes the form of a linear geometry based filter that models the shape of an action potential by taking into account its generation in the cell body / axon hillock and its propagation along the axon. The validity of the approach for different NEURON morphologies is assessed. We demonstrate that our method is able to reproduce realistic extracellular action potentials in a given range of axon/dendrites surface ratio, with a time-efficient computational burden.

Indexed as

Action PotentialsAlgorithmsAxonsComputer SimulationDendritesElectrodesElectrophysiological PhenomenaExtracellular SpaceHumansModels, NeurologicalNeuronsComputational modellingExtracellular action potentialLFP

Identifiers

What OpenQuestion holds

Textmetadata
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.