Advances in Neuromorphic Memristor Science and Applications

Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. The possibility of gathering prominent scientists in the heart of the Silicon Valley given by the 2011 International Joint Conference on Neural Networks held in San Jose, CA, has offered us the unique opportunity of organizing a series of special events on the present status and future perspectives in neuromorphic memristor science. This book presents a selection of the remarkable contributions given by the leaders of the field and it may serve as inspiration and future reference to all researchers that want to explore the extraordinary possibilities given bythis revolutionary concept.

Posited by Professor Leon Chua at UC Berkeley more than 40 years ago, memristors, a nonlinear element in electrical circuitry, are set to revolutionize computing technology. Finally discovered by scientists at Hewlett-Packard in 2008, memristors generate huge interest because they can facilitate nanoscale, real-time computer learning, as well as due to their potential of serving as instant memories. . This edited volume bottles some of the excitement about memristors, providing a state-of-the-art overview of neuromorphic memristor theory, as well as its technological and practical aspects.

Based on work presented to specialist memristor seminars organized by the editors, the volume takes readers from a general introduction the fundamental concepts involved, to specialized analysis of computational modeling, hardware, and applications. The latter include the ground-breaking potential of memristors in facilitating hybrid wetware-hardware technologies for in-vitro experiments. The book evinces, and devotes space to the discussion of, the socially transformative potential of memristors, which could be as pervasive as was the invention of the silicon chip: machines that learn in the style of brains, are a computational Holy Grail.

With contributions from key players in a fast-moving field, this edited volume is the first to cover memristors in the depth needed to trigger the further advances that surely lie around the corner.