Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Humans are complex organisms made by millions of physiological systems. Therefore,
physiological activities can represent physical or mental states of the human body …

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

Abstract Spiking Neural Networks (SNNs) are well known as a promising energy-efficient
alternative to conventional artificial neural networks. Subject to the preconceived impression …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-
temporal patterns in time series, such as the Address-Event Representation data collected …

Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence
(AI); they typically use real valued neuron responses. By contrast, biological neurons are …

Multifunctional neuromorphic devices to tackle complex tasks are highly desirable for the
development of artificial neural networks. Threshold switching (TS) memory, which exhibits …

For the progress of artificial neural networks, the imitation of multiple biological functions is
indispensable for processing more tasks in a complex working environment. Memristors …

By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material
conductance can be controlled by using an electrical input signal, which results in analog …