Memristors as electronic artificial synapses have attracted increasing attention in
neuromorphic computing. Emulation of both “learning” and “forgetting” processes requires a …

In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …

There is a growing trend to design hybrid neural networks (HNNs) by combining spiking
neural networks and artificial neural networks to leverage the strengths of both. Here, we …

Energy efficiency, parallel information processing, and unsupervised learning make the
human brain a model computing system for unstructured data handling. Different types of …

The sensory nervous system (SNS) builds up the association between external stimuli and
the response of organisms. In this system, habituation is a fundamental characteristic that …

Research on bionic eyes is of great importance for neuroprosthetics, biorobotics, and
autonomous intelligent electronics. However, implementation of an artificial visual nerve by …

The representation of external stimuli in the form of action potentials or spikes constitutes the
basis of energy efficient neural computation that emerging spiking neural networks (SNNs) …

In our brain, information is exchanged among neurons in the form of spikes where both the
space (which neuron fires) and time (when the neuron fires) contain relevant information …

Building bidirectional biointerfaces is one of the key challenges of modern engineering and
medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of …

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a
nanosecond scale that promises to enable a high-performance spintronic reservoir …