Abstract Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems
that exploit the unique physical properties of nanostructured materials. In addition to their …

Neuromorphic processing systems implementing spiking neural networks with mixed signal
analog/digital electronic circuits and/or memristive devices represent a promising …

The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

The von Neumann bottleneck has long been a significant obstacle to the advancement of
the era of intelligent computing. Neuromorphic devices are considered a promising solution …

The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

Memristive neuromorphic networks have great potential and advantage in both technology
and computational protocols for artificial intelligence. Efficient hardware design of biological …

Physical reservoir computing, which is a promising method for the implementation of highly
efficient artificial intelligence devices, requires a physical system with nonlinearity, fading …

Neuromorphic perception with event-based sensors, asynchronous hardware, and spiking
neurons shows promise for real-time, energy-efficient inference in embedded systems. Brain …

Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase
their effectiveness. Such hardware, mainly digital, is typically built on mature silicon nodes …

Oxide-based resistive random access memory (RRAM) is standing out in both non-volatile
memory and the emerging field of neuromorphic computing, with the consequence of …