Bayes optimal template matching for spike sorting -combining fisher discriminant analysis with optimal filtering

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• 作者：Felix Franke ; Rodrigo Quian Quiroga…
• 关键词：Spike sorting ; Extracellular recording ; Signal processing ; Overlap ; Linear filtering ; Linear discriminant analysis ; Matched filtering
• 刊名：Journal of Computational Neuroscience
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：38
• 期：3
• 页码：439-459
• 全文大小：4,292 KB
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  Kim, S., & McName
• 作者单位：Felix Franke (1) (2)
  Rodrigo Quian Quiroga (3)
  Andreas Hierlemann (1)
  Klaus Obermayer (2)
  
  1. ETH Zürich, Department of Biosystems Science and Engineering, Basel, 4058, Switzerland
  2. School for Electrical Engineering and Computer Science, Technische Universit?t Berlin, Berlin, Germany
  3. Centre for Systems Neuroscience, University of Leicester, Leicester, UK
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Neurosciences
  Neurology
  Human Genetics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1573-6873

文摘

Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy.