The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

With their working mechanisms based on ion migration, the switching dynamics and
electrical behaviour of memristive devices resemble those of synapses and neurons, making …

Neuromorphic computing based on emerging devices could overcome the von Neumann
bottleneck—the restriction created by having to transfer data between memory and …

Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …

Neuromorphic perception systems inspired by biology have tremendous potential in
efficiently processing multi-sensory signals from the physical world, but a highly efficient …

The global demand for data storage and processing has increased exponentially in recent
decades. To respond to this demand, research efforts have been devoted to the …

As a key building block of biological cortex, neurons are powerful information processing
units and can achieve highly complex nonlinear computations even in individual cells …

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual
neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate …

Physiological signal processing plays a key role in next-generation human-machine
interfaces as physiological signals provide rich cognition-and health-related information …

Developing a single-phase self-rectifying memristor with the continuously tunable feature is
structurally desirable and functionally adaptive to dynamic environmental stimuli variations …