Elsevier

Journal of Neuroscience Methods

Volume 293, 1 January 2018, Pages 264-283
Journal of Neuroscience Methods

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

https://doi.org/10.1016/j.jneumeth.2017.10.007Get rights and content
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Highlights

  • New VSD and Ca2+-imaging techniques allow high-resolution imaging of neurons.

  • We present a stepwise model-fitting scheme with possibilities to apply to such data.

  • We apply our method to simulated data to construct a reduced-morphology L5PC model.

  • Our model is cost-efficient and reproduces the main features of the original model.

  • Our model predicts that interconnected L5PCs can amplify low-frequency inputs.

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.

Keywords

Multi-compartmental neuron models
Biophysically detailed modeling
Model fitting using imaging data
Automated fitting methods
Parameter peeling

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