Independent Component Analysis for Functional Neuronal Interactions

摘要：

Blind separation of independent sources or independent component analysis (ICA) has received a great deal of attention in the field of neurobioiogical data analysis such as EEC, MEG, fMRI. In this paper, we present a novel result for identification of neuronal ensemble interactions by applying ICA approach. The experimental results are obtained based on the cortical circuit model. Several kinds of neuronal activities such as the back-ground and afferent activities have been identified successfully. This result suggests that the source signals are represented in the correlated firing patterns within the specific range. Neuronal activities can be detected when high-order correlations between them are quantified by ICA.

作者: Akira Yoshida Tooru Nakagawa Jianting Cao Shoji Tanaka

作者单位: Lab for Artificial Brain Systems, Dept. of Electrical and Electronics Engineering, Sophia University Lab for Artificial Brain Systems, Dept. of Electrical and Electronics Engineering, Sophia University

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1441-1446

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）