摘要
振荡现象以动态形式普遍存在于神经系统中,并且与大脑的信息处理、传递和整合、巩固记忆等高级认知活动密切相关。神经振荡的特定活动模式往往关联认知功能及其变化,因此如何量化分析神经振荡活动模式成为了计算神经生物学的研究热点之一。结合作者实验室近年来的研究工作,本文对在神经生物学和认知科学研究中常用的多种分析算法进行了详细而全面的综述,并试图按照度量指标及耦合或同步方式的差异进行归类。通过算法比较,给出计算特点及算法适用情形。最后对将来有潜在应用价值的几种多维算法进行了深入的探讨。
Neural oscillatory phenomenon generally exists in the nervous system through a dynamic form. It plays a very important role in the brain, especially in the higher cognitive activities, such as information processing, transfer and integration, consolidating memory and so on. Furthermore, the specific activity pattern of neural oscillations is often associated with cognitive functions and their alterations. Accordingly, how to quantitatively analyze the pattern of neural oscillations becomes one of the fundamental issues in the computational neuroscience. In this review, we addressed a variety of analytic algorithms, which are commonly employed in our recent studies to investigate the issues of neurobiology and cognitive science. In addition, we tried to classify these analytic algo- rithms by distinguishing their different metrics, synchronization for potential application have also been discussed. and coupling modes. Finally, multidimensional analytic algorithms
出处
《生理学报》
CAS
CSCD
北大核心
2015年第2期143-154,共12页
Acta Physiologica Sinica
基金
supported by the National Natural Science Foundation of China(No.31171053
11232005)
关键词
神经网络
振荡分析算法
信息流
振荡同步
neural network
analytic algorithms
neural information flow
oscillatory synchronization
