Neuromorphic context-dependent learning framework with fault-tolerant spike routing
Journal Publication ResearchOnline@JCUAbstract
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
Publication Name
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Volume
33
ISBN/ISSN
2162-2388
Edition
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Issue
12
Pages Count
15
Location
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Publisher
Institute of Electrical and Electronics Engineers
Publisher Url
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Publisher Location
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Publish Date
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Url
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Date
N/A
EISSN
N/A
DOI
10.1109/TNNLS.2021.3084250