A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells

Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)
    31 Downloads (Pure)

    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.

    Original languageEnglish
    Pages (from-to)264-283
    Number of pages20
    JournalJournal of Neuroscience Methods
    Volume293
    Early online date7 Oct 2017
    DOIs
    Publication statusPublished - 1 Jan 2018

    Keywords

    • Animals
    • Automation, Laboratory/methods
    • Calcium/metabolism
    • Cerebral Cortex/cytology
    • Computer Simulation
    • Dendrites/physiology
    • Image Processing, Computer-Assisted/methods
    • Ion Channels/metabolism
    • Membrane Potentials/physiology
    • Models, Neurological
    • Pattern Recognition, Automated
    • Potassium/metabolism
    • Pyramidal Cells/cytology
    • Synapses/physiology
    • Voltage-Sensitive Dye Imaging/methods

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