SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture
By a News Reporter-Staff News Editor at Life Science Weekly -- New research on Networks - Neural Networks and Learning Systems is the subject of a report. According to news reporting out of Guilin, People’s Republic of China, by NewsRx editors, research stated, “Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR).”
Funders for this research include National Natural Science Foundation of China, Guangxi Natural Science Foundation, Research Project of Guangxi University of China, Guangxi Key Lab of Multi-source Information Mining & Security, Doctoral Research Foundation of Guangxi Normal University.
Our news journalists obtained a quote from the research from Guangxi Normal University, “When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing.”
According to the news editors, the research concluded: “Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.”
For more information on this research see: SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture. IEEE Transactions on Neural Networks and Learning Systems , 2018;29(4):1287-1300. IEEE Transactions on Neural Networks and Learning Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Neural Networks and Learning Systems - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72)
Our news journalists report that additional information may be obtained by contacting J.X. Liu, Guangxi Normal Univ, Fac Elect Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 41004, People’s Republic of China. Additional authors for this research include J. Harkin, L.P. Maguire, L.J. McDaid and J.J. Wade.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TNNLS.2017.2673021. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-10), Findings on Neural Networks and Learning Systems Reported by Investigators at Guangxi Normal University (SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture), Life Science Weekly, 839, ISSN: 1552-2474, BUTTER® ID: 015448177
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