Change-point detection in neuronal spike train activity

被引:20
作者
Ratnam, R
Goense, JBM
Nelson, ME
机构
[1] Univ Illinois, Beckman Inst Adv Sci & Technol, Urbana, IL 61801 USA
[2] Univ Illinois, Ctr Biophys & Computat Biol, Urbana, IL 61801 USA
[3] Univ Illinois, Dept Mol & Integrat Physiol, Urbana, IL 61801 USA
关键词
spike train analysis; signal detection; change-point detection; CUSUM; neural coding;
D O I
10.1016/S0925-2312(02)00815-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
Animals respond to changes in their environment based on the information encoded in neuronal spike activity. One key issue is to determine how quickly and reliably the system can detect that a behaviorally relevant change has taken place. What are the neural mechanisms and computational principles that allow fast, reliable detection of changes in spike activity? Here we present an optimal statistical signal-processing algorithm for change-point detection, known as the cumulative sum (CUSUM) algorithm. We then show that the performance of a simple neuron model with leaky-integrate-and-fire dynamics can approach theoretically optimal performance limits under certain conditions. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:849 / 855
页数:7
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