At the end of a rush lasting over half a century, in which CMOS technology has been
experiencing a constant and breathtaking increase of device speed and density, Moore's …

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological
synaptic plasticity and learning are described. The material properties and electrical …

Memristors and memristor crossbar arrays have been widely studied for neuromorphic and
other in-memory computing applications. To achieve optimal system performance, however …

Time-series analysis including forecasting is essential in a range of fields from finance to
engineering. However, long-term forecasting is difficult, particularly for cases where the …

Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …

Reservoir computing systems utilize dynamic reservoirs having short-term memory to project
features from the temporal inputs into a high-dimensional feature space. A readout function …

Reinforcement learning algorithms that use deep neural networks are a promising approach
for the development of machines that can acquire knowledge and solve problems without …

The close replication of synaptic functions is an important objective for achieving a highly
realistic memristor-based cognitive computation. The emulation of neurobiological learning …

Memristive devices are a class of physical systems with history-dependent dynamics
characterized by signature hysteresis loops in their input–output relations. In the past few …

Memristors have been extensively studied for data storage and low-power computation
applications. In this study, we show that memristors offer more than simple resistance …