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Cereb Cortex. 2016 Dec;26(12):4461-4496. doi: 10.1093/cercor/bhw237. Epub 2016 Oct 20.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

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Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.
Department of Psychology, University of Oslo, 0373 Oslo, Norway.
Department of Neuroscience and Pharmacology, University of Copenhagen, 2200 Copenhagen, Denmark.
Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, 100 44 Stockholm, Sweden.
Theoretical Systems Neurobiology, RWTH Aachen University, 52056 Aachen, Germany.
Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany.
Department of Physics, Faculty 1, RWTH Aachen University, 52062 Aachen, Germany.
Department of Physics, University of Oslo, 0316 Oslo, Norway.


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 ∼1 mm2 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.


cortical microcircuit; electrostatic forward modeling; extracellular potential; multicompartment neuron modeling; point-neuron network models

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