Memristive devices for brain-inspired computing : from materials, devices, and circuits to applications : computational memory, deep learning, and spiking neural networks / edited by Sabina Spiga [and three others].
Contributor(s): Spiga, Sabina [editor.]
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Material type: 
BookSeries: Woodhead Publishing series in electronic and optical materials.Publisher: Duxford ; Cambridge, MA : Woodhead Publishing, [2020]Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 0081027826; 9780081027820; 9780081027875; 0081027877.Subject(s): Neural networks (Computer science) | Memristors | Neural Networks, Computer | R�eseaux neuronaux (Informatique) | Memristances | Memristors | Neural networks (Computer science)Additional physical formats: Print version:: No titleDDC classification: 006.3/2 Online resources: ScienceDirect Summary: Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications-Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists.
Includes bibliographical references and index.
Online resource; title from digital title page (viewed on June 30, 2020).
Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications-Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists.
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