Low power hardware algorithms and architectures for spike sorting and detection

Abstract

A neuronal recording system featuring a large number of electrodes and a portable wireless front-end integrated circuit for signal processing for low-power spike detection and alignment. The system is configured as a Neuroprocessor and introduces hardware architectures for automatic spike detection and alignment algorithms. The Neuroprocessor can be placed next to the recording electrodes and provide for all stages of spike processing, stimulating neuronal tissues and wireless communications to a host computer. Some of the algorithms are based on principal component analysis (PCA). Others employ a novel Integral Transform. The algorithms execute autonomously, but require off-line training and setting of computational parameters. Pre-recorded neuronal signals evaluate the accuracy of the proposed algorithms and architectures: The recorded data are processed by a standard PCA spike sorting software algorithm, as well as by the several hardware algorithms, and the outcomes are compared.