Improving spike separation using waveform derivatives.
This paper presents spike derivatives as a tool for spike feature extraction to improve the separation of similar neurons. The theoretical framework of neuronal geometry signatures and noise shaping to perform the spike derivative is formulated first, and based on the derivations we show that the first derivative of the spikes manifests the waveform difference contributed by the geometry signatures and also reduces the associated low-frequency noise. Quantitative comparisons of sorting neurons using spikes and their derivatives are performed on spike sequences from a public database, and improved results are observed when using spike derivatives.
Yang
Zhi
Z
School of Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA. yangzhi@soe.ucsc.edu
Zhao
Qi
Q
Liu
Wentai
W
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001479:Basal Ganglia / Q000502:physiology; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D000882:Haplorhini; D019579:Neocortex / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D025341:Principal Component Analysis; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted