{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hybrid-scheme-for-modeling-local-field","title":"Hybrid scheme for modeling local field potentials from point-neuron networks","arxiv_id":"1511.01681","date":"2016-01-20","proceeding":null,"authors":[],"abstract":"Due to rapid advances in multielectrode recording technology, the local field\npotential (LFP) has again become a popular measure of neuronal activity in both\nbasic research and clinical applications. Proper understanding of the LFP\nrequires detailed mathematical modeling incorporating the anatomical and\nelectrophysiological features of neurons near the recording electrode, as well\nas synaptic inputs from the entire network. Here we propose a hybrid modeling\nscheme combining the efficiency of commonly used simplified point-neuron\nnetwork models with the biophysical principles underlying LFP generation by\nreal neurons. The scheme can be used with an arbitrary number of point-neuron\nnetwork populations. The LFP predictions rely on populations of\nnetwork-equivalent, anatomically reconstructed multicompartment neuron models\nwith layer-specific synaptic connectivity. The present scheme allows for a full\nseparation of the network dynamics simulation and LFP generation. For\nillustration, we apply the scheme to a full-scale cortical network model for a\n$\\sim$1 mm$^2$ patch of primary visual cortex and predict laminar LFPs for\ndifferent network states, assess the relative LFP contribution from different\nlaminar populations, and investigate the role of synaptic input correlations\nand neuron density on the LFP. The generic nature of the hybrid scheme and its\npublicly available implementation in \\texttt{hybridLFPy} form the basis for LFP\npredictions from other point-neuron network models, as well as extensions of\nthe current application to larger circuitry and additional biological detail.","url_abs":"http://arxiv.org/abs/1511.01681v2","url_pdf":"http://arxiv.org/pdf/1511.01681v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hybrid-scheme-for-modeling-local-field","repo_url":"https://github.com/INM-6/hybridLFPy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}