Neuromorphic computing is a brain-inspired computing paradigm that aims to construct
efficient, low-power, and adaptive computing systems by emulating the information …

The sensory nervous system of humans mainly depends on continuous training and memory
to improve the pain-perceptional abilities for the complex noxious information in the real …

Physical reservoir computing (PRC) is thought to be a potential low training-cost temporal
processing platform, which has been explored by the nonlinear and volatile dynamics of …

Exploitation of low‐dimensional metal‐oxide semiconductor nanowires (MOS NWs) with
peculiar and radial coaxial architectures is of great significance for constructing nanoscale …

In recent years, the implementation of neuromorphic computation on devices has attracted
much attention. Bienenstock–Cooper–Munro (BCM) learning rule is considered as the most …

Bio‐inspired synaptic devices have garnered considerable interest in neuromorphic
computing. The Bienenstock‐Cooper‐Munro (BCM) learning rule stands out as one of the …

In this study, optoelectronic synaptic transistors based on indium–gallium–zinc oxide (IGZO)
with a casein electrolyte-based electric double layer (EDL) were examined. The casein …

In this letter, we propose neuromorphic transistors employing indium oxide (In2O3)
nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be …

In memristors, the implementation of the Bienenstock–Cooper–Munro (BCM) learning rule
plays a significant role in the modulation balance of artificial synapses and the reduction of …

Fear neural circuits can recognize precisely threatening stimuli and enable the early-
warning for the individual in the real world. In this regard, implementation of fear neural …