The quest to imbue machines with intelligence akin to that of humans, through the
development of adaptable neuromorphic devices and the creation of artificial neural …

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the
fundamental mismatches between AI models, which rely on parallel, in-memory, and …

Increasing demand for bio-interfaced human-machine interfaces propels the development of
organic neuromorphic electronics with small form factors leveraging both ionic and …

The traditional von Neumann structure computers cannot meet the demands of high-speed
big data processing; therefore, neuromorphic computing has received a lot of interest in …

Neurons, which are made of biological tissue, exhibit cognitive properties that can be
replicated in various material substrates. To create brain-inspired computational artificial …

Threshold switching (TS) memristors are promising candidates for artificial neurons in
neuromorphic systems. However, they often lack biological plausibility, typically functioning …

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate
neuronal excitability, is key to spatial attention of biological neural systems. In‐memory …

Neuromorphic perception and computing show great promise in terms of energy efficiency
and data bandwidth compared to von Neumann's computing architecture. In-sensor …

The human brain efficiently processes only a fraction of visual information, a phenomenon
termed attentional control, resulting in energy savings and heightened adaptability …

Neural circuits with specific structures and diverse neuronal firing features are the
foundation for supporting intelligent tasks in biology and are regarded as the driver for …