Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models - Ladd, Alexander; Kim, Kyung Geun; Balewski, Jan; Bouchard, Kristofer; Ben-Shalom, Roy (Frontiers in Neuroinformatics, 2022)
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 100 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.