A quantitative analysis of the performance of computing architectures used in neural simulations

摘要

Background: Numerical solutions of neuron models are helping neuroscientists gain new insights into the behavior of neural systems. Although computing power is increasing, the complexity of the systems being simulated is also increasing. If the computation is not well matched to the computing hardware, simulations can take lengthy times to run, which can make it more difficult to draw inferences from those simulations and also to use them in feedback with living neurons such as in the dynamic clamp.