[2205.10122] Stochastic resonance neurons in artificial neural networks
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Computer Science > Neural and Evolutionary Computing

arXiv:2205.10122 (cs)
[Submitted on 6 May 2022 (v1), last revised 23 Aug 2022 (this version, v2)]

Title:Stochastic resonance neurons in artificial neural networks

Authors:Egor Manuylovich, Diego Argüello Ron, Morteza Kamalian-Kopae, Sergei Turitsyn
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Abstract:Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the challenge of noise accumulation. We propose here a new type of neural networks using stochastic resonances as an inherent part of the architecture and demonstrate a possibility of significant reduction of the required number of neurons for a given performance accuracy. We also show that such a neural network is more robust against the impact of noise.
Subjects: Neural and Evolutionary Computing (cs.NE); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
Cite as: arXiv:2205.10122 [cs.NE]
  (or arXiv:2205.10122v2 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2205.10122

Submission history

From: Diego Argüello Ron [view email]
[v1] Fri, 6 May 2022 18:42:36 UTC (1,696 KB)
[v2] Tue, 23 Aug 2022 12:02:23 UTC (2,078 KB)
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