Memristor Device and Memristive System
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microelectronics".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 612
Special Issue Editors
Interests: memristive neural networks; modeling of memristive devices; spiking neural networks; neuromorphic computing
2. Engineering Department, University of Messina, 98122 Messina, Italy
Interests: spiking neural networks; neuromorphic computing; neurosimulation; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue of Electronics, entitled “Memristor Device and Memristive System”, is dedicated to both theory and applications of memristors and memristive devices and systems, especially in the domain of brain-inspired computing, i.e., neuromorphic computing. Starting with the theoretical foundations for the fourth fundamental circuit element, postulated in the work of Prof. Leon Chua in 1971, followed by an influential paper from 2008 by the HP Labs group led by Stanley Williams, memristors continue to attract extensive research interest. With the ability to support multilevel resistance states, inherent plasticity, high scalability potential, and energy-efficient operation modes, memristive devices are promising building blocks in neuromorphic computing.
This Special Issue aims to collate original research papers in the domain of mathematical models of memristive devices (including memristors, memcapacitors, and meminductors), design and application of memristive circuits, ReRAM, memristive-based neurons and synapses, training algorithms for memristive neural networks (MNNs) and memristive spiking neural networks (MSNN), neuromorphic computing, and the design and application of bio-inspired computing architectures.
Research areas of interest within this Special Issue include, but are not limited to, the following:
- Training algorithms for memristive neural networks and memristive spiking neural networks;
- Theoretical modeling of memristive devices;
- Artificial neural networks based on memristors;
- Memristive neuron and synapse for SNNs and MSNNs;
- Unconventional computing with memristors;
- Neuromorphic circuits;
- Bio-inspired algorithms and systems;
- In-memory computing;
- Applications of memristors in AI;
- Complex networks with memristors;
- Application of memristive circuits in biomedical engineering.
Dr. Nataša Samardžić
Dr. Max Talanov
Guest Editors
Manuscript Submission Information
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Keywords
- memristive devices
- memristive neural networks
- theoretical models
- neuromorphic computing
- memristive neurons and synapses
- spiking neural networks
- memristive circuits
- bio-inspired algorithms
- nonlinear circuits
- in-memory computing
- complex networks
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