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Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks
Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany.;Norwegian Univ Life Sci, Dept Math Sci & Technol, N-1403 As, Norway..
Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany..ORCID iD: 0000-0002-7664-916X
Norwegian Univ Life Sci, Dept Math Sci & Technol, N-1403 As, Norway.;Univ Oslo, Dept Psychol, N-0373 Oslo, Norway..ORCID iD: 0000-0003-2063-7861
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Univ Copenhagen, Dept Neurosci & Pharmacol, DK-2200 Copenhagen, Denmark.;Royal Inst Technol, Sch Comp Sci & Commun, Dept Computat Biol, S-10044 Stockholm, Sweden..ORCID iD: 0000-0001-5630-4095
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2016 (English)In: Cerebral Cortex, ISSN 1047-3211, E-ISSN 1460-2199, Vol. 26, no 12, p. 4461-4496Article in journal (Refereed) Published
Abstract [en]

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a similar to 1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS INC , 2016. Vol. 26, no 12, p. 4461-4496
Keywords [en]
cortical microcircuit, electrostatic forward modeling, extracellular potential, multicompartment neuron modeling, point-neuron network models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-242601DOI: 10.1093/cercor/bhw237ISI: 000392864500005PubMedID: 27797828Scopus ID: 2-s2.0-85009231679OAI: oai:DiVA.org:kth-242601DiVA, id: diva2:1291877
Note

QC 20190226

Hittar inte belägg för att Henrik Lindén är från KTH

Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-06-12Bibliographically approved

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Dahmen, DavidStavrinou, Maria L.Lindén, Henrikvan Albada, Sacha J.Diesmann, Markus
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