GP-GPU: Bridging the Gap between Modelling & Experimentation

Abstract

Within the field of neural electrophysiology, there exists a divide between experimentalists and computational modellers. This is caused by the different spheres of expertise required to perform each discipline, as well as the differing resource requirements of the two parties. This paper considers several forms of hardware acceleration for implementation within a laboratory alongside time sensitive experimentation, and focuses on how the use of general purpose computation on graphics processing units (GP-GPU) can allow parameter estimation to be performed in the laboratory, thereby acting as a bridge between the two halves of this field.This would facilitate rapid iterative model design, as well as allowing new forms of experimentation. This discussion is concluded with a brief case study that reports the performance increases associated with a GPU implementation over a single CPU approach. It should be noted that the proposed paradigm is not limited to neuroscience, as it would be beneficial to any discipline where unreliable time sensitive experimental procedures dominate exploration of the field.