Towards robust inference of biomedical signals in energy-efficient neuromorphic networks

Abstract

Computation capability characteristics of neuromorphic analog/mixed-signal spiking neural networks offer capable platform for implementation of cognitive tasks on resource-limited embedded platforms. In this paper, we derive stochastic model of spiking neural processing systems for energy-efficient recognition and inference of biomedical systems. We examine imperfections in the network dynamics and noise-induced information processing, influence of the uncertainty on the behavior of the emulated networks, and impact on the clustering accuracy of cardiac arrhythmia. Experimental results indicate that stochasticity at networks connections is a adequate resource for deep learning machines.