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dc.contributor.authorMäki-Marttunen, Tuomo
dc.contributor.authorHalnes, Geir
dc.contributor.authorDevor, Anna
dc.contributor.authorMetzner, Christoph
dc.contributor.authorDale, Anders M.
dc.contributor.authorAndreassen, Ole A.
dc.contributor.authorEinevoll, Gaute T.
dc.date.accessioned2018-04-11T16:21:39Z
dc.date.available2018-04-11T16:21:39Z
dc.date.issued2018-01-01
dc.identifier.citationMäki-Marttunen , T , Halnes , G , Devor , A , Metzner , C , Dale , A M , Andreassen , O A & Einevoll , G T 2018 , ' A stepwise neuron model fitting procedure designed for recordings with high spatial resolution : Application to layer 5 pyramidal cells ' , Journal of Neuroscience Methods , vol. 293 , pp. 264-283 . https://doi.org/10.1016/j.jneumeth.2017.10.007
dc.identifier.issn0165-0270
dc.identifier.urihttp://hdl.handle.net/2299/19975
dc.description© 2017 The Author(s). Published by Elsevier B. V. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.description.abstractBackground 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.en
dc.format.extent20
dc.format.extent6048800
dc.language.isoeng
dc.relation.ispartofJournal of Neuroscience Methods
dc.subjectAnimals
dc.subjectAutomation, Laboratory/methods
dc.subjectCalcium/metabolism
dc.subjectCerebral Cortex/cytology
dc.subjectComputer Simulation
dc.subjectDendrites/physiology
dc.subjectImage Processing, Computer-Assisted/methods
dc.subjectIon Channels/metabolism
dc.subjectMembrane Potentials/physiology
dc.subjectModels, Neurological
dc.subjectPattern Recognition, Automated
dc.subjectPotassium/metabolism
dc.subjectPyramidal Cells/cytology
dc.subjectSynapses/physiology
dc.subjectVoltage-Sensitive Dye Imaging/methods
dc.titleA stepwise neuron model fitting procedure designed for recordings with high spatial resolution : Application to layer 5 pyramidal cellsen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85031754984&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.jneumeth.2017.10.007
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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