神经模型中的随机共振研究进展  

Research Progress of Stochastic Resonance in Neural Models

作　　者：梁晓冰[1] 刘希顺[1] 刘安芝[1] 王博亮[2] 

机构地区：[1]国防科技大学电子科学与技术系,长沙410073 [2]厦门大学计算机科学系,厦门361005

出　　处：《生物医学工程学杂志》2009年第4期912-916,共5页Journal of Biomedical Engineering

摘　　要：随机共振是一种常见于非线性系统中的由适当的噪声引起的系统最优响应现象。神经系统含有噪声,相关生物实验和理论研究均证明噪声有助于神经信号的检测和处理,且当前对神经系统信息处理与存储机制的研究是一大热点。文章回顾了最近发表的关于随机共振的研究成果,从噪声、随机共振的引申概念和网络模型三个方面,总结了目前对神经模型中随机共振的研究进展,并简单讨论了这类研究的发展趋势。In nonlinear systems, noise can improve the responses of the systems with appropriate noise intensity. This phenomenon is called stochastic resonance. Biological neural systems are noisy and stochastic resonance has been found in them experimentally and theoretically. Now many researches focus on the signal transmission and processing in neural models. So this paper introduces the researches of stochastic resonance in noisy neural models. Then the recent research achievement and progress are reviewed in the following three aspects; noise; the development of stochastic resonance; and neural network. At last, the foreground of the study is discussed.

关 键 词：神经模型 随机共振 噪声 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]