Chow Carson C, Buice Michael A
Mathematical Biology Section, Laboratory of Biological Modeling, NIDDK, NIH, Bethesda, MD 20892 USA.
J Math Neurosci. 2015 Mar 24;5:8. doi: 10.1186/s13408-015-0018-5. eCollection 2015.
Stochastic differential equations (SDEs) have multiple applications in mathematical neuroscience and are notoriously difficult. Here, we give a self-contained pedagogical review of perturbative field theoretic and path integral methods to calculate moments of the probability density function of SDEs. The methods can be extended to high dimensional systems such as networks of coupled neurons and even deterministic systems with quenched disorder.
随机微分方程（SDEs）在数学神经科学中有多种应用，而且众所周知很难求解。在此，我们对微扰场论和路径积分方法进行了一个自包含的教学性综述，以计算随机微分方程概率密度函数的矩。这些方法可以扩展到高维系统，如耦合神经元网络，甚至是具有淬火无序的确定性系统。
