title: Algorithms and architectures for low power spike detection and alignment.
abstract: 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.
authors.affiliation: VLSI Systems Research Center, Technion-Israel Institute of Technology, Haifa 3200, Israel.
authors.forename: Alex
authors.initials: A
authors.lastname: Zviagintsev
authors.forename: Yevgeny
authors.initials: Y
authors.lastname: Perelman
authors.forename: Ran
authors.initials: R
authors.lastname: Ginosar
mesh_terms: 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*