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PLoS Comput Biol. 2015 Dec 14;11(12):e1004584. doi: 10.1371/journal.pcbi.1004584. eCollection 2015 Dec.

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

Author information

1
The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy.
2
Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.
3
Department of Neuroscience and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
4
Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology-KTH, Stockholm, Sweden.
5
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt/Main, Germany.
6
Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt/Main, Germany.
7
Frankfurt Institute for Advanced Studies (FIAS), Frankfurt/Main, Germany.
8
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway.
9
Department of Physics, University of Oslo, Oslo, Norway.

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.

PMID:
26657024
PMCID:
PMC4682791
DOI:
10.1371/journal.pcbi.1004584
[Indexed for MEDLINE]
Free PMC Article

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