Burkitt A N
The Bionic Ear Institute, 384-388 Albert Street, East Melbourne, VIC 3002, Australia.
Biol Cybern. 2006 Aug;95(2):97-112. doi: 10.1007/s00422-006-0082-8. Epub 2006 Jul 5.
The integrate-and-fire neuron model describes the state of a neuron in terms of its membrane potential, which is determined by the synaptic inputs and the injected current that the neuron receives. When the membrane potential reaches a threshold, an action potential (spike) is generated. This review considers the model in which the synaptic input varies periodically and is described by an inhomogeneous Poisson process, with both current and conductance synapses. The focus is on the mathematical methods that allow the output spike distribution to be analyzed, including first passage time methods and the Fokker-Planck equation. Recent interest in the response of neurons to periodic input has in part arisen from the study of stochastic resonance, which is the noise-induced enhancement of the signal-to-noise ratio. Networks of integrate-and-fire neurons behave in a wide variety of ways and have been used to model a variety of neural, physiological, and psychological phenomena. The properties of the integrate-and-fire neuron model with synaptic input described as a temporally homogeneous Poisson process are reviewed in an accompanying paper (Burkitt in Biol Cybern, 2006).
积分发放神经元模型根据神经元的膜电位来描述其状态，膜电位由神经元接收的突触输入和注入电流决定。当膜电位达到阈值时，就会产生动作电位（尖峰）。本综述考虑了这样一种模型，其中突触输入呈周期性变化，并由非齐次泊松过程描述，同时存在电流突触和电导突触。重点是能够分析输出尖峰分布的数学方法，包括首达时间方法和福克-普朗克方程。最近对神经元对周期性输入的响应的关注部分源于对随机共振的研究，随机共振是指噪声引起的信噪比增强。积分发放神经元网络表现出各种各样的行为，并已被用于对各种神经、生理和心理现象进行建模。一篇随附论文（Burkitt，《生物控制论》，2006年）综述了将突触输入描述为时间齐次泊松过程的积分发放神经元模型的特性。
