低相关更新输入影响Integrate-and-Fire模型的输出  

Low Correlation Between Renewal Process Synaptic Inputs Impacts on the Output of the Integrate-and-Fire Model

作　　者：陈内萍[1,2] 黄新 邓迎春[2] 

机构地区：[1]湖南商学院信息系 [2]湖南师范大学数学与计算机科学学院

出　　处：《湖南师范大学自然科学学报》2005年第4期27-31,共5页Journal of Natural Science of Hunan Normal University

基　　金：湖南省教育厅资助项目(04C357)

摘　　要：研究了随机更新输入的IF神经元模型的近似问题,得到了两种新的近似方案.讨论了低相关更新输入对Integrate-and-Fire模型输出的影响.对低的正相关,随着输入相关的增加,平均发放时间缩短.对低的负相关,平均发放时间独立于输入相关.The issue how to approximate the integrate-and-fire（IF） model with renewal process inputs is studied. Two novel approximation schemes are proposed. The effects of positive and negative correlations are discussed, mean firing time is a decreasing function of input correlation. For low negative correlations, mean firing time is almost independent of input correlation.

关 键 词：Integrate-and-Fire模型 更新过程 放电脉冲时间间隔 相关 

分 类 号：O211[理学—概率论与数理统计]