Tuomo Mäki-Marttunen1, Geir Halnes2, Anna Devor3, Christoph Metzner4, Anders M Dale5, Ole A Andreassen6, Gaute T Einevoll7. 1. NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Simula Research Laboratory, Lysaker, Norway. Electronic address: tuomo@simula.no. 2. Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway. 3. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. 4. Biocomputation Research Group, University of Hertfordshire, Hatfield, UK. 5. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA. 6. NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. 7. Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway; Department of Physics, University of Oslo, Oslo, Norway.
Abstract
BACKGROUND: Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. NEW METHOD: In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. RESULT: We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. COMPARISON WITH EXISTING METHODS: Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. CONCLUSIONS: The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.
BACKGROUND: Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. NEW METHOD: In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. RESULT: We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. COMPARISON WITH EXISTING METHODS: Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. CONCLUSIONS: The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.
Authors: Yiyang Gong; Cheng Huang; Jin Zhong Li; Benjamin F Grewe; Yanping Zhang; Stephan Eismann; Mark J Schnitzer Journal: Science Date: 2015-11-19 Impact factor: 47.728
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Authors: Tuomo Mäki-Marttunen; Nicolangelo Iannella; Andrew G Edwards; Gaute T Einevoll; Kim T Blackwell Journal: Elife Date: 2020-07-30 Impact factor: 8.140
Authors: Tuomo Mäki-Marttunen; Anna Devor; William A Phillips; Anders M Dale; Ole A Andreassen; Gaute T Einevoll Journal: Front Comput Neurosci Date: 2019-09-26 Impact factor: 2.380
Authors: Tuomo Mäki-Marttunen; Florian Krull; Francesco Bettella; Espen Hagen; Solveig Næss; Torbjørn V Ness; Torgeir Moberget; Torbjørn Elvsåshagen; Christoph Metzner; Anna Devor; Andrew G Edwards; Marianne Fyhn; Srdjan Djurovic; Anders M Dale; Ole A Andreassen; Gaute T Einevoll Journal: Cereb Cortex Date: 2019-02-01 Impact factor: 5.357
Authors: Tuomo Mäki-Marttunen; Tobias Kaufmann; Torbjørn Elvsåshagen; Anna Devor; Srdjan Djurovic; Lars T Westlye; Marja-Leena Linne; Marcella Rietschel; Dirk Schubert; Stefan Borgwardt; Magdalena Efrim-Budisteanu; Francesco Bettella; Geir Halnes; Espen Hagen; Solveig Næss; Torbjørn V Ness; Torgeir Moberget; Christoph Metzner; Andrew G Edwards; Marianne Fyhn; Anders M Dale; Gaute T Einevoll; Ole A Andreassen Journal: Front Psychiatry Date: 2019-08-06 Impact factor: 4.157