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Edge of Chaos in Memristor Cellular Nonlinear Networks

Author

Listed:
  • Angela Slavova

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

  • Ventsislav Ignatov

    (Laboratory of Engineering Mathematics, Ruse University “Angel Kanchev”, 7017 Ruse, Bulgaria)

Abstract

Information processing in the brain takes place in a dense network of neurons connected through synapses. The collaborative work between these two components (Synapses and Neurons) allows for basic brain functions such as learning and memorization. The so-called von Neumann bottleneck, which limits the information processing capability of conventional systems, can be overcome by the efficient emulation of these computational concepts. To this end, mimicking the neuronal architectures with silicon-based circuits, on which neuromorphic engineering is based, is accompanied by the development of new devices with neuromorphic functionalities. We shall study different memristor cellular nonlinear networks models. The rigorous mathematical analysis will be presented based on local activity theory, and the edge of chaos domain will be determined in the models under consideration. Simulations of these models working on the edge of chaos will show the generation of static and dynamic patterns.

Suggested Citation

  • Angela Slavova & Ventsislav Ignatov, 2022. "Edge of Chaos in Memristor Cellular Nonlinear Networks," Mathematics, MDPI, vol. 10(8), pages 1-11, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1288-:d:792471
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    References listed on IDEAS

    as
    1. Joel Hochstetter & Ruomin Zhu & Alon Loeffler & Adrian Diaz-Alvarez & Tomonobu Nakayama & Zdenka Kuncic, 2021. "Avalanches and edge-of-chaos learning in neuromorphic nanowire networks," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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