Effects of Sorting Error on the Frequency Characteristics of a Spike Train
Date Issued
2014
Date
2014
Author(s)
Lin, Yu-Ting
Abstract
Spike sorting as a topic in the field of neuron science is a crucial portion on analyzing neuronal activities. For various algorithms and conditions, the spike sorting is impossible free of errors during the classification. The spike sorting errors produce a great impact on the analysis of neural signals, such as causality and entropy. Here, we concerned on the effects of the spike sorting errors on influencing the frequency characteristic of a spike train whenever a time-frequency analysis used to be applied on the analysis of the pattern on spectrogram of spike train. In the time-frequency analysis, it is not straight forward for an uneven spike train which means that the sampling intervals are not identical, for instance, Short Time Fourier Transform (STFT) cannot be directly applied. Fortunately, a new method of time–frequency analysis for un-evenly sampled signals called “Weighted Wavelet Z-transform (WWZ)” has been developed for analyzing the period of a pulsar in astronomy in recent. In this study, we will introduce WWZ to neuron science and demonstrate its performance and reliability for time-frequency analysis on neural spike train through simulations. We construct some neural spike train model and introduce several types of errors on the proposed models. Then using WWZ to analyze them, we further compare the spectrograms with difference spike sorting errors. Through this study, our observation results could be a useful guideline for neuroscientists on spike sorting approach.
Subjects
Action Potential
Spike Sorting Error
Time-Frequency Analysis
Weighted Wavelet Z-transfrom
Type
thesis
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