Neuromorphic Spike Data Classifier for Reconfigurable Brain-Machine Interface

Abstract

In this paper, we propose a reconfigurable neural spike classifier based on neuromorphic event-based networks that can be directly interfaced to neural signal conditioning and quantization circuits. The classifier is set as a heterogeneity based, multi-layer computational network to offer wide flexibility in the implementation of plastic and metaplastic interactions, and to increase efficacy in neural signal processing. Built-in temporal control mechanisms allow the implementation of homeostatic regulation in the resulting network. The results obtained in a 90 nm CMOS technology show that an efficient neural spike data classification can be obtained with a low power (9.4 μW/core) and compact (0.54 mm2 per core) structure.