论文标题
矩神经激活的有效数值算法
An efficient numerical algorithm for the moment neural activation
论文作者
论文摘要
通过扩散近似源自尖峰神经元模型，矩激活（MA）忠实地捕获了相关神经变异性的非线性耦合。但是，由于多种条件类似的道森函数，对MA的数值评估面临重大挑战。通过得出这些功能的渐近扩展，我们开发了一种有效的数值算法来评估MA及其衍生物，以确保可靠性，速度和准确性。我们还为MA提供了精确的分析表达式，以弱波动极限。通过这种有效的算法，MA可以作为研究大规模尖峰神经回路中神经变异性动态的有效工具。
Derived from spiking neuron models via the diffusion approximation, the moment activation (MA) faithfully captures the nonlinear coupling of correlated neural variability. However, numerical evaluation of the MA faces significant challenges due to a number of ill-conditioned Dawson-like functions. By deriving asymptotic expansions of these functions, we develop an efficient numerical algorithm for evaluating the MA and its derivatives ensuring reliability, speed, and accuracy. We also provide exact analytical expressions for the MA in the weak fluctuation limit. Powered by this efficient algorithm, the MA may serve as an effective tool for investigating the dynamics of correlated neural variability in large-scale spiking neural circuits.
