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Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity
Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions. However, there is a lack of learning algorithms for complex SNNs with recurrent connections, comparable in efficiency with back-propagation techniques an...
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| Pubblicato in: | Front Neuroinform |
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| Autori principali: | , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Frontiers Media S.A.
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6250118/ https://ncbi.nlm.nih.gov/pubmed/30498439 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fninf.2018.00079 |
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