Literature DB >> 31953614

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

Harry Tran1, Radu Ranta2, Steven Le Cam1, Valérie Louis-Dorr1.   

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.

Keywords:  Computational modelling; Extracellular action potential; LFP

Mesh:

Year:  2020        PMID: 31953614     DOI: 10.1007/s10827-019-00735-3

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  58 in total

Review 1.  A review of methods for spike sorting: the detection and classification of neural action potentials.

Authors:  M S Lewicki
Journal:  Network       Date:  1998-11       Impact factor: 1.273

2.  Action potential generation requires a high sodium channel density in the axon initial segment.

Authors:  Maarten H P Kole; Susanne U Ilschner; Björn M Kampa; Stephen R Williams; Peter C Ruben; Greg J Stuart
Journal:  Nat Neurosci       Date:  2008-01-20       Impact factor: 24.884

Review 3.  Modelling and analysis of local field potentials for studying the function of cortical circuits.

Authors:  Gaute T Einevoll; Christoph Kayser; Nikos K Logothetis; Stefano Panzeri
Journal:  Nat Rev Neurosci       Date:  2013-11       Impact factor: 34.870

4.  In vivo correlation between axon diameter and conduction velocity in the human brain.

Authors:  Assaf Horowitz; Daniel Barazany; Ido Tavor; Moran Bernstein; Galit Yovel; Yaniv Assaf
Journal:  Brain Struct Funct       Date:  2014-08-20       Impact factor: 3.270

5.  On the relation between fibre diameter and conduction velocity in myelinated nerve fibres.

Authors:  J M Ritchie
Journal:  Proc R Soc Lond B Biol Sci       Date:  1982-12-22

6.  Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

Authors:  Espen Hagen; David Dahmen; Maria L Stavrinou; Henrik Lindén; Tom Tetzlaff; Sacha J van Albada; Sonja Grün; Markus Diesmann; Gaute T Einevoll
Journal:  Cereb Cortex       Date:  2016-10-20       Impact factor: 5.357

Review 7.  Past, present and future of spike sorting techniques.

Authors:  Hernan Gonzalo Rey; Carlos Pedreira; Rodrigo Quian Quiroga
Journal:  Brain Res Bull       Date:  2015-04-27       Impact factor: 4.077

8.  Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEG.

Authors:  Klas H Pettersen; Henrik Lindén; Tom Tetzlaff; Gaute T Einevoll
Journal:  PLoS Comput Biol       Date:  2014-11-13       Impact factor: 4.475

9.  Bio-inspired benchmark generator for extracellular multi-unit recordings.

Authors:  Sirenia Lizbeth Mondragón-González; Eric Burguière
Journal:  Sci Rep       Date:  2017-02-24       Impact factor: 4.379

10.  NetPyNE, a tool for data-driven multiscale modeling of brain circuits.

Authors:  Salvador Dura-Bernal; Benjamin A Suter; Padraig Gleeson; Matteo Cantarelli; Adrian Quintana; Facundo Rodriguez; David J Kedziora; George L Chadderdon; Cliff C Kerr; Samuel A Neymotin; Robert A McDougal; Michael Hines; Gordon Mg Shepherd; William W Lytton
Journal:  Elife       Date:  2019-04-26       Impact factor: 8.140

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  1 in total

Review 1.  From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.

Authors:  Réka Barbara Bod; János Rokai; Domokos Meszéna; Richárd Fiáth; István Ulbert; Gergely Márton
Journal:  Front Neuroinform       Date:  2022-06-13       Impact factor: 3.739

  1 in total

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