This Handbook presents all aspects of memristor networks in an easy to read and tutorial style. Including many colour illustrations, it covers the foundations of memristor theory and applications, the technology of memristive devices, revised models of the Hodgkin-Huxley Equations and ion channels, neuromorphic architectures, and analyses of the dynamic behaviour of memristive networks. It also shows how to realise computing devices, non-von Neumann architectures and provides future building blocks for deep learning hardware.
With contributions from leaders in computer science, mathematics, electronics, physics, material science and engineering, the book offers an indispensable source of information and an inspiring reference text for future generations of computer scientists, mathematicians, physicists, material scientists and engineers working in this dynamic field.
Author Biography:
Leon Chua is a Professor in Electrical and Computer Science at Berkeley. His research interests include Cellular Neural/Nonlinear Networks, Nonlinear Circuits and Systems, Nonlinear Dynamics, Bifurcation and Chaos.
Dr. Georgios Ch. Sirakoulis is an Associate Professor with tenure in the Department of Electrical and Computer Engineering, Democritus University of Thrace. His research interests include Nanoelectronics and nanotechnology, future and emergent electronic devices, circuits, models and architectures (memristors, quantum cellular automata etc.), Novel and Emergent micro-nano systems and circuits, beyond CMOS computing devices and circuits, Memristors, Green and Unconventional computing, High performance Computing, Novel paradigms of computing, Cyber-Physical and Embedded Systems, Bioinspired computation/ biocomputation and bioengineering, Cellular Automata Theory and Applications, FPGAs, Modelling and Simulation, Complex systems.
Andrew Adamatzky is a Professor in Unconventional Computing in the Department of Computer Science, Director of the Unconventional Computing Centre, and a member of Bristol Robotics Lab. His research is in reaction-diffusion computing, cellular automata, physarum computing, massive parallel computation, applied mathematics, collective intelligence and robotics.
