Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. 2016

Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
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.

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.

UI MeSH Term Description Entries
D008564 Membrane Potentials The voltage differences across a membrane. For cellular membranes they are computed by subtracting the voltage measured outside the membrane from the voltage measured inside the membrane. They result from differences of inside versus outside concentration of potassium, sodium, chloride, and other ions across cells' or ORGANELLES membranes. For excitable cells, the resting membrane potentials range between -30 and -100 millivolts. Physical, chemical, or electrical stimuli can make a membrane potential more negative (hyperpolarization), or less negative (depolarization). Resting Potentials,Transmembrane Potentials,Delta Psi,Resting Membrane Potential,Transmembrane Electrical Potential Difference,Transmembrane Potential Difference,Difference, Transmembrane Potential,Differences, Transmembrane Potential,Membrane Potential,Membrane Potential, Resting,Membrane Potentials, Resting,Potential Difference, Transmembrane,Potential Differences, Transmembrane,Potential, Membrane,Potential, Resting,Potential, Transmembrane,Potentials, Membrane,Potentials, Resting,Potentials, Transmembrane,Resting Membrane Potentials,Resting Potential,Transmembrane Potential,Transmembrane Potential Differences
D008959 Models, Neurological Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment. Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009433 Neural Inhibition The function of opposing or restraining the excitation of neurons or their target excitable cells. Inhibition, Neural
D009474 Neurons The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM. Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D002540 Cerebral Cortex The thin layer of GRAY MATTER on the surface of the CEREBRAL HEMISPHERES that develops from the TELENCEPHALON and folds into gyri and sulci. It reaches its highest development in humans and is responsible for intellectual faculties and higher mental functions. Allocortex,Archipallium,Cortex Cerebri,Cortical Plate,Paleocortex,Periallocortex,Allocortices,Archipalliums,Cerebral Cortices,Cortex Cerebrus,Cortex, Cerebral,Cortical Plates,Paleocortices,Periallocortices,Plate, Cortical
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D013788 Thalamus Paired bodies containing mostly GRAY MATTER and forming part of the lateral wall of the THIRD VENTRICLE of the brain. Thalamencephalon,Thalamencephalons

Related Publications

Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
January 2016, Frontiers in computational neuroscience,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
August 2016, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
April 2019, Journal of neural engineering,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
August 2023, ISA transactions,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
January 2001, IEEE transactions on neural networks,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
May 2016, Journal of neuroscience methods,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
October 2020, Journal of neuroscience methods,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
May 2006, The Journal of chemical physics,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
March 2008, Journal of neurophysiology,
Espen Hagen, and David Dahmen, and Maria L Stavrinou, and Henrik Lindén, and Tom Tetzlaff, and Sacha J van Albada, and Sonja Grün, and Markus Diesmann, and Gaute T Einevoll
April 2016, The Journal of neuroscience : the official journal of the Society for Neuroscience,
Copied contents to your clipboard!