Wavelet-based neural pattern analyzer for behaviorally significant burst pattern recognition.
Closed-loop neural prosthesis systems rely on accurately recording neural data from multiple neurons and detecting behaviorally meaningful patterns before representing them in a highly compressed form for wireless transmission over a limited-bandwidth link. We present a novel wavelet-based approach for detecting spikes, grouping them as bursts and building a dynamic vocabulary of meaningful burst patterns. Simulation results on pre-recorded in vivo multi-channel extracellular neural data from the buccal ganglion of Aplysia demonstrate the feasibility of behavior recognition as well as data compression (>500X) by the proposed approach.
Narasimhan
Seetharam
S
Case Western Reserve University, Cleveland, OH, USA. sxn124@case.edu
Cullins
Miranda
M
Chiel
Hillel J
HJ
Bhunia
Swarup
S
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001048:Aplysia; D001185:Artificial Intelligence; D001522:Behavior, Animal / Q000502:physiology*; D001683:Biological Clocks / Q000502:physiology; D003679:Deglutition / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*