Monitoring spike train synchrony

被引:97
|
作者
Kreuz, Thomas [1 ]
Chicharro, Daniel [2 ]
Houghton, Conor [3 ]
Andrzejak, Ralph G. [4 ]
Mormann, Florian [5 ]
机构
[1] CNR, Inst Complex Syst, I-50019 Sesto Fiorentino, Italy
[2] Italian Inst Technol, Ctr Neurosci & Cognit Syst UniTn, Rovereto, TN, Italy
[3] Univ Bristol, Dept Comp Sci, Bristol, Avon, England
[4] Univ Pompeu Fabra, Dept Informat & Commun Technol, Barcelona, Spain
[5] Univ Bonn, Dept Epileptol, Bonn, Germany
关键词
data analysis; synchronization; spike trains; clustering; SPIKE-distance; VISUAL-CORTEX; DYNAMICS; STATE;
D O I
10.1152/jn.00873.2012
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F. Monitoring spike train synchrony. J Neurophysiol 109: 1457-1472, 2013. First published December 5, 2012; doi:10.1152/jn.00873.2012.-Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.
引用
收藏
页码:1457 / 1472
页数:16
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