Novel hardware and concepts for unconventional computing
The potential of machine learning can only be fully exploited if more efficient hardware is developed that meets the special needs of bio-inspired computing schemes. In this respect, non-volatile memory technology using memristive devices in combination with neuromorphic systems is a promising way to such hardware. This Collection provides a platform for interdisciplinary research on unconventional computing with new physical substrates.
Editors
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Martin Ziegler
TU Ilmenau, Germany
Martin Ziegler is a Professor at TU Ilmenau, Germany, and head of the Department of Microelectronic and Nanoelectronic Systems. His research interests include the development of memristive devices and their integration in neuromorphic circuits, as well as electronic transport measurements, thin film analysis techniques, and the development of neural computational models. Martin Ziegler has been an Editorial Board Member for Scientific Reports since 2017.
You can also read about Prof Ziegler's experience as a Guest Editor for this Collection by reading their post on the Nature Research Devices and Materials Engineering Community, and find 'Behind the Paper' posts from authors of papers included in the Collection, and other authors, here.
