Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications. Advances in Nonlinear Dynamics and Robotics (ANDC)

Table of Contents

Part I: Mem-elements and their emulators

1. The fourth circuit element was found: a brief history 2. Implementing memristor emulators in hardware 3. On the FPGA implementation of chaotic oscillators based on memristive circuits 4. Microwave memristive components for smart RF front-end modules 5. The modeling of memcapacitor oscillator motion with ANN and its nonlinear control application 6. Rich dynamics of memristor based Li�nard systems 7. Hidden extreme multistability generated from a novel memristive two-scroll chaotic system 8. Extreme multistability, hidden chaotic attractors and amplitude controls in an absolute memristor Van der Pol-Duffing circuit: dynamical analysis and electronic implementation 9. Memristor-based novel 4D chaotic system without equilibria 10. Memristor Helmholtz oscillator: analysis, electronic implementation, synchronization and chaos control using single controller 11. Design guidelines for physical implementation of fractional-order integrators and its application in memristive systems 12. Control of bursting oscillations in memristor based Wien-bridge oscillator

Part II: Applications of mem-elements

13. Memristor, mem-systems and neuromorphic applications: a review 14. Guidelines for benchmarking non-ideal analog memristive crossbars for neural networks 15. Bipolar resistive switching in biomaterials: case studies of DNA and melanin-based bio-memristive devices 16. Nonvolatile memristive logic: a road to in-memory computing 17. Implementation of organic RRAM with ink-jet printer: from design to using in RFID-based application 18. Neuromorphic vision networks for face recognition 19. Synaptic devices based on HfO2 memristors 20. Analog circuit integration of backpropagation learning in memristive HTM architecture 21. Multi-stable patterns coexisting in memristor synapse-coupled Hopfield neural network 22. Fuzzy memristive networks 23. Fuzzy integral sliding mode technique for synchronization of memristive neural networks 24. Robust adaptive control of fractional-order memristive neural networks 25. Learning memristive spiking neurons and beyond

Authors

Christos Volos Associate Professor, Aristotle University of Thessaloniki, Greece. Christos Volos received the Physics Diploma degree, the M.Sc. degree in electronics, and the Ph.D. degree in chaotic electronics from the Physics Department, Aristotle University of Thessaloniki, in 1999, 2002, and 2008, respectively. He is currently an Associate Professor with the Physics Department, Aristotle University of Thessaloniki, Greece and a member of the Laboratory of Nonlinear Circuits - Systems & Complexity (LaNSCom). Viet-Thanh Pham Director of Research, Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, Vietnam. Viet-Thanh Pham is the Director of Research at Faculty of Electrical and Electronic Engineering, Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, Vietnam. He received the degree in electronics and telecommunications from the Hanoi University of Technology, Vietnam, in 2005, and the Ph.D. degree in electronics, automation and control of complex systems engineering from the University of Catania, Italy, in 2013. He was a postdoctoral researcher at the Division of Dynamics, Lodz University of Technology, Poland.