Model calibration using a parallel differential evolution algorithm in computational neuroscience: simulation of stretch induced nerve deficit

TL;DR

This paper presents a parallel differential evolution algorithm to efficiently calibrate a neuronal damage model, enabling faster simulation of nerve deficits after mechanical injury, with improved accuracy over manual methods.

Contribution

It introduces a parallel implementation of differential evolution for model calibration in computational neuroscience, reducing computation time and improving calibration accuracy.

Findings

Parallel DE outperforms manual calibration in speed and accuracy.

Model simulates gradual nerve damage with complex averaging.

Speed-up achieved by simplifying initial stretch simulation.

Abstract

Neuronal damage, in the form of both brain and spinal cord injuries, is one of the major causes of disability and death in young adults worldwide. One way to assess the direct damage occurring after a mechanical insult is the simulation of the neuronal cells functional deficits following the mechanical event. In this study, we use a coupled mechanical electrophysiological model with several free parameters that are required to be calibrated against experimental results. The calibration is carried out by means of an evolutionary algorithm (differential evolution, DE) that needs to evaluate each configuration of parameters on six different damage cases, each of them taking several minutes to compute. To minimise the simulation time of the parameter tuning for the DE, the stretch of one unique fixed-diameter axon with a simplified triggering process is used to speed up the calculations. The model is then leveraged for the parameter optimization of the more realistic bundle of independent axons, an impractical configuration to run on a single processor computer. To this end, we have developed a parallel implementation based on OpenMP that runs on a multi-processor taking advantage of all the available computational power. The parallel DE algorithm obtains good results, outperforming the best effort achieved by published manual calibration, in a fraction of the time. While not being able to fully capture the experimental results, the resulting nerve model provides a complex averaging framework for nerve damage simulation able to simulate gradual axonal functional alteration in a bundle.