Special Issue "Memristor Cellular Nonlinear Networks: Theory and Applications"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 30 September 2022 | Viewed by 787

Special Issue Editors

Prof. Dr. Angela Slavova
E-Mail Website
Guest Editor
Mathematical Physics Department, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad.G.Bonchev str. 8, 1113 Sofia, Bulgaria
Interests: cellular neural networks; differential equations; modeling; numerical methods
Prof. Dr. Ronald Tetzlaff
E-Mail Website
Guest Editor
Institute of Circuits and Systems, Technische Universität Dresden, Dresden, Germany
Interests: circuit theory; memristors; chaotic circuits; cellular neural networks (CNNs); deep learning; biomedical signal processing
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Special Issue Information

Dear colleagues,

Further development of memristor-based cellular nonlinear networks (MCNN), including conventional applications, is necessary from the point of view of the current market need for new nanoelectronic circuit architectures. MCNNs working on the edge of chaos can exhibit very complex behavior. The application of a new excitable medium in investigations to detect the global motion of excitable waves, and transferring this to the analysis of more complex systems such as brain networks and social networks, is particularly challenging. 

In this Special Issue, the following topics will be covered: 

- MCNNs operating on edge of chaos; 
- Simulations of MCNNs operating on edge of chaos regime; 
- Pattern formation in MCNN models; 
- Simulation of MCNNs operating on edge of chaos regime; 
- Applications of MCNNs.

Theoretical and simulation results for MCNNs will be in complete concordance, demonstrating that conventional, very large-scale integration technology could be an ideal medium for studying the complex behavior of different models.

Prof. Dr. Angela Slavova
Prof. Dr. Ronald Tetzlaff
Guest Editors

Manuscript Submission Information

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Keywords

  • cellular nonlinear networks
  • memristor
  • local activity theory
  • edge of chaos
  • dynamical behavior
  • chaotic systems
  • patter formation.

Published Papers (1 paper)

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Research

Article
Edge of Chaos in Memristor Cellular Nonlinear Networks
Mathematics 2022, 10(8), 1288; https://doi.org/10.3390/math10081288 - 12 Apr 2022
Cited by 1 | Viewed by 343
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Memristor Cellular Nonlinear Networks: Theory and Applications)
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