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.
Original language | English |
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Title of host publication | 2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 261-262 |
Number of pages | 2 |
ISBN (Electronic) | 9781728105437 |
DOIs | |
Publication status | Published - 1 Mar 2019 |
Event | 1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019 - Osaka, Japan Duration: 12 Mar 2019 → 14 Mar 2019 |
Conference
Conference | 1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019 |
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Country/Territory | Japan |
City | Osaka |
Period | 12/03/19 → 14/03/19 |
Keywords
- Biomedical signals data classification
- Neuromorphic computing
- Noise
- Robust inference
- SNN
- Uncertainty