Various hypotheses of information representation in brain, referred to as neural codes, have
been proposed to explain the information transmission between neurons. Neural coding …

There is an increasing interest in emulating Spiking Neural Networks (SNNs) on
neuromorphic computing devices due to their low energy consumption. Recent advances …

Spiking neural networks (SNNs) are shown to be more biologically plausible and energy
efficient over their predecessors. However, there is a lack of an efficient and generalized …

A self‐powered artificial mechanoreceptor module is demonstrated with a triboelectric
nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a …

Neuromorphic computing (NC) architecture inspired by biological nervous systems has
been actively studied to overcome the limitations of conventional von Neumann …

A ferroelectric thin‐film transistor (FeTFT)‐based synaptic device with an indium–gallium–
zinc oxide (IGZO) channel and a metal–ferroelectric–metal–insulator–semiconductor …

Soft wearable strain sensors with mechanically invisible interactions with skin tissue have
enabled precise diagnosis and effective treatment of neurological movement disorders in a …

Brain-inspired computing and neuromorphic hardware are promising approaches that offer
great potential to overcome limitations faced by current computing paradigms based on …

To process data operations more efficiently in deep neural networks (DNNs), studies on
spiking neural networks (SNNs) have been conducted. In the reported literature, CMOS …

Neural networks have been widely used for advanced tasks from image recognition to
natural language processing. Many recent works focus on improving the efficiency of …