A Reconfigurble Hebbian Eigenfilter for Neurophysiological Spike Train Analysis

The emergence of microelectrode array enables the study of real-time neurophysiological activities across multiple regions of the brain. However, the real-time extracellular voltage potentials recorded on any electrode represent the simultaneous electrical activity of an unknown number of neurons which present a critical challenge to the accuracy of interpretation and identification of the neural circuitry in the subsequent analysis. In this paper, we present a principal component analysis approach utilizing Hebbian eigenfilter to identify the corresponding electrical activities to each neuron, or namely spike sorting. The Hebbian eigenfilter greatly simplifies the computational complexity of eigen-projection. An efficient and low-power hardware architecture is proposed. The performance, accuracy and power consumption of our Hebbian eigenfilter are thoroughly evaluated through synthetic spike trains. The proposal enables real-time spike sorting and analysis, and leads the way towards future motor and cognitive neuroprosthetics.