期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 54, 期 2, 页码 262-272出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2006.886934
关键词
information theory; microelectrode recording; single-unit recording; spike discrimination; spike sorter; vision
During extracellular electrophysiological recording experiments, the waveform of neuronal spikes recorded from a single neuron often changes. These spike-waveform changes make single-neuron identification difficult, particularly when the activities of multiple neurons are simultaneously recorded with a multichannel microelectrode, such as a tetrode or a heptode. We have developed a tracking method of individual neurons despite their changing spike amplitudes. The method is based on a bottom-up hierarchical clustering algorithm that tracks each neuron's spike cluster during temporally overlapping clustering periods. We evaluated this method by comparing spike sorting with and without cluster tracking of an identical series of multineuronal spikes recorded from monkey area-TE neurons responding to a set of visual stimuli. According to Shannon's information theory, errors in spike-amplitude tracking reduce the expected value of the amount of information about a stimulus set that is transferred by the spike train of a cluster. In this study, cluster tracking significantly increased the expected value of the amount of information transferred by a spike train (p < 0.01). Additionally, the stability of the stimulus preference and that of the cross-correlation between clusters improved significantly (p < 0.000001). We conclude that cluster tracking improves the quality of multineuronal data analysis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据