Neuromorphic context-dependent learning framework with fault-tolerant spike routing

Journal Publication ResearchOnline@JCU
Yang, Shuangming;Wang, Jiang;Deng, Bin;Rahimi Azghadi, Mostafa;Linares-Barranco, Bernabe
Abstract

Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning.

Journal

IEEE Transactions on Neural Networks and Learning Systems

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Volume

33

ISBN/ISSN

2162-2388

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Issue

12

Pages Count

15

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Publisher

Institute of Electrical and Electronics Engineers

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Date

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EISSN

N/A

DOI

10.1109/TNNLS.2021.3084250