@ARTICLE{10.3389/fninf.2022.882552, AUTHOR={Ladd, Alexander and Kim, Kyung Geun and Balewski, Jan and Bouchard, Kristofer and Ben-Shalom, Roy}, TITLE={Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models}, JOURNAL={Frontiers in Neuroinformatics}, VOLUME={16}, YEAR={2022}, URL={https://www.frontiersin.org/articles/10.3389/fninf.2022.882552}, DOI={10.3389/fninf.2022.882552}, ISSN={1662-5196}, ABSTRACT={Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.} }