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.

Original languageEnglish
Title of host publication2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages261-262
Number of pages2
ISBN (Electronic)9781728105437
DOIs
Publication statusPublished - 1 Mar 2019
Event1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019 - Osaka, Japan
Duration: 12 Mar 201914 Mar 2019

Conference

Conference1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019
CountryJapan
CityOsaka
Period12/03/1914/03/19

    Research areas

  • Biomedical signals data classification, Neuromorphic computing, Noise, Robust inference, SNN, Uncertainty

ID: 66955097