TY  - JOUR
A1  - Mazzoni, Alberto
A1  - Lindén, Henrik
A1  - Cuntz, Hermann
A1  - Lansner, Anders
A1  - Panzeri, Stefano
A1  - Einevoll, Gaute
T1  - Computing the local field potential (LFP) from integrate-and-fire network models
T2  - Plos Computational Biology, volume 11, issue 12, e1004584 (2015)
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.
Y1  - 2015
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/39226
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-392265
SN  - 1553-734X
SN  - 1553-7358
N1  - Copyright: © 2015 Mazzoni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
VL  - 11
IS  - (12): e1004584
SP  - 1
EP  - 38
PB  - PLoS
CY  - Lawrence, Kan.
ER  -