Abstract
In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.
Original language | English |
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Title of host publication | Neural Information Processing |
Subtitle of host publication | 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part VI (Theoretical Computer Science and General Issues) |
Editors | Derong Liu, Shengli Xie, Yuanqing Li |
Publisher | Springer, Cham |
Pages | 384-392 |
Number of pages | 9 |
ISBN (Electronic) | 9783319701363 |
ISBN (Print) | 9783319701356 |
DOIs | |
Publication status | Published - 26 Oct 2017 |
Externally published | Yes |
Event | 24th International Conference On Neural Information Processing - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017 Conference number: 24 http://www.apnns.org/ICONIP2017/ (Link to Conference Website) |
Conference
Conference | 24th International Conference On Neural Information Processing |
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Abbreviated title | ICONIP 2017 |
Country/Territory | China |
City | Guangzhou |
Period | 14/11/17 → 18/11/17 |
Internet address |
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