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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.
Submissions are welcome on a rolling basis. Find out how to submit on this page.
All articles have undergone Scientific Reports' standard peer review process and have been subject to all of the journal’s standard policies. This includes the journal’s policy on competing interests. The Guest Editor declares no competing interests with the submissions which they have handled through the peer review process. The peer review of any submissions for which the Guest Editor has competing interests is handled by another Editorial Board Member who has no competing interests.
This Collection has not been supported by sponsorship.
Neuromorphic systems are currently experiencing a rapid upswing due to the fact that today's CMOS (complementary metal oxide silicon) based technologies are increasingly approaching their limits. In particular, for the area of machine learning, energy consumption of today's electronics is an important limitation, that also contributes toward the ever-increasing impact of digitalization on our climate. Thus, in order to better meet the special requirements of unconventional computing, new physical substrates for bio-inspired computing schemes are extensively exploited. The aim of this Guest Edited Collection is to provide a platform for interdisciplinary research along three main lines: memristive materials and devices, emulation of cellular learning (neurons and synapses), and unconventional computing and network schemes.