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Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

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Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. / Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

in: PLOS COMPUT BIOL, Jahrgang 11, Nr. 12, 14.12.2015, S. e1004584.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Mazzoni, A, Lindén, H, Cuntz, H, Lansner, A, Panzeri, S & Einevoll, GT 2015, 'Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models', PLOS COMPUT BIOL, Jg. 11, Nr. 12, S. e1004584. https://doi.org/10.1371/journal.pcbi.1004584

APA

Mazzoni, A., Lindén, H., Cuntz, H., Lansner, A., Panzeri, S., & Einevoll, G. T. (2015). Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. PLOS COMPUT BIOL, 11(12), e1004584. https://doi.org/10.1371/journal.pcbi.1004584

Vancouver

Mazzoni A, Lindén H, Cuntz H, Lansner A, Panzeri S, Einevoll GT. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. PLOS COMPUT BIOL. 2015 Dez 14;11(12):e1004584. https://doi.org/10.1371/journal.pcbi.1004584

Bibtex

@article{d1c1c39f0c514029b9c47f9527e9ee53,
title = "Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models",
abstract = "Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best {"}LFP proxy{"}, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with {"}ground-truth{"} LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. ",
keywords = "Action Potentials/physiology, Brain/physiology, Brain Mapping/methods, Computer Simulation, Electromagnetic Fields, Humans, Membrane Potentials/physiology, Models, Neurological, Nerve Net/physiology, Neurons/physiology, Synaptic Transmission/physiology",
author = "Alberto Mazzoni and Henrik Lind{\'e}n and Hermann Cuntz and Anders Lansner and Stefano Panzeri and Einevoll, {Gaute T}",
year = "2015",
month = dec,
day = "14",
doi = "10.1371/journal.pcbi.1004584",
language = "English",
volume = "11",
pages = "e1004584",
journal = "PLOS COMPUT BIOL",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

RIS

TY - JOUR

T1 - Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

AU - Mazzoni, Alberto

AU - Lindén, Henrik

AU - Cuntz, Hermann

AU - Lansner, Anders

AU - Panzeri, Stefano

AU - Einevoll, Gaute T

PY - 2015/12/14

Y1 - 2015/12/14

N2 - Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

AB - Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

KW - Action Potentials/physiology

KW - Brain/physiology

KW - Brain Mapping/methods

KW - Computer Simulation

KW - Electromagnetic Fields

KW - Humans

KW - Membrane Potentials/physiology

KW - Models, Neurological

KW - Nerve Net/physiology

KW - Neurons/physiology

KW - Synaptic Transmission/physiology

U2 - 10.1371/journal.pcbi.1004584

DO - 10.1371/journal.pcbi.1004584

M3 - SCORING: Journal article

C2 - 26657024

VL - 11

SP - e1004584

JO - PLOS COMPUT BIOL

JF - PLOS COMPUT BIOL

SN - 1553-734X

IS - 12

ER -