论文标题
统计场理论和尖峰神经元网络
Statistical Field Theory and Networks of Spiking Neurons
论文作者
论文摘要
本文在统计字段理论的框架内对大量相互作用的神经元的动力学进行了建模。我们使用最初在统计字段理论[44]的背景下开发的方法，后来适合相互作用中的复杂系统[45] [46]。我们的模型跟踪单个相互作用的神经元动力学，但也保留了神经场动力学的某些特征和目标，例如通过空间变量索引大量神经元。因此，本文桥接了个体相互作用的神经元的规模和神经场理论的宏观尺度建模。
This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the features and goals of neural field dynamics, such as indexing a large number of neurons by a space variable. Thus, this paper bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.
