An Algorithm for Spike Sorting of Mice Neuronal Signals Recorded by Tetrode

• Main Authors: Heng-Wei Chang, 張�睆�
• Other Authors: Ta-Te Lin
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/14630830940827033816

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 96 === The PSPCA (parallel spike principal component analysis) method developed in this study can efficiently extract both spatial and waveform feature from a spike (action po-tential) simultaneously. Affinity propagation (AP) clustering algorithm with those fea-tures is used for spike sorting of neuronal signals. PSPCA is based on principal compo-nent analysis (PCA) and the signal decay function of the distance between neuronal spike source and tetrode. Spikes are sorted using AP clustering algorithm with similarity matrix computed from those features. According to the simulation results with different signal noise ratios (S/N ratio), waveform feature is highly correlated with original spike pattern and can be regarded as denoised spike. Comparing the Davies-Bouldin validity index (DBVI) value of waveform feature with three other features, peak, peak ratio, and serial spike principal component (SSPC), the performance and stability of waveform feature are better than that of other features. We used spatial feature as weighting value of similarity matrix computed from waveform feature for AP clustering. As a result, AP clustering determined the amount of clusters automatically and gave reasonable results that are not dependent on experimenter’s experience. By tuning the parameters of AP, preference and damping factor, the over-sorting results can be avoided. Comparing ad-justed Rand index of AP with k-means, AP is about 38% higher than k-means method in accuracy under different S/N ratios. Also, clustering number error of AP is about 67% lower than that of the k-means method. Finally, the PSPCA spike sorting algorithm was applied to 48 experimental tetrode signals recorded from mice RT and VPL. There are 1~10 units sorted out from these data. As indicate above, we conclude that the PSPCA algorithm is useful for sorting spikes recorded by tetrode and performs better results than the k-means spike sorting algorithm.