Algorithms and architectures for low power spike detection and alignment.
We introduce algorithms and architectures for automatic spike detection and alignment that are designed for low power. Some of the algorithms are based on principal component analysis (PCA). Others employ a novel integral transform analysis and achieve 99% of the precision of a PCA detector, while requiring only 0.05% of the computational complexity. The algorithms execute autonomously, but require off-line training and setting of computational parameters. We employ pre-recorded neuronal signals to evaluate the accuracy of the proposed algorithms and architectures: the recorded data are processed by a standard PCA spike detection and alignment software algorithm, as well as by the several hardware algorithms, and the outcomes are compared.
Zviagintsev
Alex
A
VLSI Systems Research Center, Technion-Israel Institute of Technology, Haifa 3200, Israel.
Perelman
Yevgeny
Y
Ginosar
Ran
R
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D019736:Prostheses and Implants; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*