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Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Copenhagen, Denmark.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
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2015 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 12, e1004584Article in journal (Refereed) PublishedText
Abstract [en]

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.

Place, publisher, year, edition, pages
Public Library of Science , 2015. Vol. 11, no 12, e1004584
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-181204DOI: 10.1371/journal.pcbi.1004584ISI: 000368521900014ScopusID: 2-s2.0-84953301873OAI: oai:DiVA.org:kth-181204DiVA: diva2:902292
Funder
EU, FP7, Seventh Framework Programme
Note

Funding Details: FP7-284553, EC, European Commission

QC 20160210. QC 20160216

Available from: 2016-02-10 Created: 2016-01-29 Last updated: 2016-02-16Bibliographically approved

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Lindén, HenrikLansner, Anders
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