A neuromorphic neural spike clustering processor for deep-brain sensing and stimulation systems

Abstract

This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas the conventional hardware based on the Osort algorithm achieves 69% for the same datasets. Implemented in a 65nm high-Vth, the processor exhibits a footprint of 0.25mm2/ch. and a power consumption of 9.3μW/ch. at VDD of 0.3V.