Efficient fitting of conductance-based model neurons from somatic current clamp

Authors:
Nathan F. Lepora;Paul G. Overton;Kevin Gurney
Affiliations:
Department of Psychology, University of Sheffield, Sheffield, UK S10 2TP;Department of Psychology, University of Sheffield, Sheffield, UK S10 2TP;Department of Psychology, University of Sheffield, Sheffield, UK S10 2TP
Venue:
Journal of Computational Neuroscience
Year:
2012

Citing 6
Cited 0

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Automated neuron model optimization techniques: a review

Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
The role of action potential shape and parameter constraints in optimization of compartment models

Neurocomputing
Simple model of spiking neurons

IEEE Transactions on Neural Networks

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Abstract

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin---Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.