Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Front Neuroinform. 2022 Jun 17:16:882552. doi: 10.3389/fninf.2022.882552. eCollection 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 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.

Keywords: biophysical neuron model; electrophysiology; evolutionary algorithms; high performance computing; non-convex optimization; strong scaling; weak scaling.