Title: A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells
Authors: Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M Dale, Ole A Andreassen and Gaute T Einevoll
Journal: Journal of Neuroscience Methods 293 (2018) 264–283
Abstract:
Background: Recent progress in electrophysiological and optical methods for neuronal recordings pro-vides vast amounts of high-resolution data. In parallel, the development of computer technology hasallowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these develop-ments is the construction of single-cell and network models in a way that faithfully reproduces neuronalbiophysics 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 neuronmodel to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) andCa2+imaging.
Result: We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a modelwith reduced neuronal morphology. We connect the reduced-morphology neurons into a network andvalidate against simulated data from a high-resolution L5PC network model.
Comparison with existing methods: Our approach combines features from several previously appliedmodel-fitting strategies. The reduced-morphology neuron model obtained using our approach reliablyreproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphologymodel.
Conclusions: The network models produced using our method are cost-efficient and predict that inter-connected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizesand topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SKcurrent.