Results 81 to 90 of about 28,095 (262)

Bubble Formation Control: Fabrication of Centimeter‐Sized Tissue‐Like Constructs by Catalase‐Coated Oxygen‐Releasing Hydrogel

Advanced Materials, EarlyView.
Oxygen‐releasing hydrogels are widely used to support cell survival in 3D cultures and to promote wound healing. However, incorporating catalase to convert H2O2 into O2 often generates additional oxygen bubbles, leading to material instability which rarely addressed.
Sukulya Bunuasunthon, Isabel F. Amaral, Ana P. Pêgo, Michiya Matsusaki   +3 more
wiley   +1 more source

Optoelectronic Nanofluidic Neural Networks for Ionic Computing

Advanced Materials, EarlyView.
An ion‐based optoelectronic nanofluidic memristor enables neuromorphic computing in aqueous environments. With tunable ionic memory and multimodal synaptic plasticity, it realizes densely connected ionic neural networks capable of image classification, motion prediction, logic computation, and real‐time in‐sensor computing, advancing fully connected ...
Yaxin Huang, Zhiyuan Du, Changjin Wu, Binglin Zeng, Yingnan Cao, Mingliang Li, Liren Zhang, Qingxin Guo, Chao Huang, Can Li, Jinyao Tang   +10 more
wiley   +1 more source

Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning

Neural Plasticity, 2020
Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification.
Xiumin Li, Hao Yi, Shengyuan Luo
doaj   +1 more source

Evolutionary spiking neural networks: a survey

Journal of Membrane Computing
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the unique information propagation mechanisms and the complexity of SNN neuron models pose challenges for adopting traditional methods developed for ANNs to SNNs.
Shuaijie Shen, Rui Zhang 0042, Chao Wang, Renzhuo Huang, Aiersi Tuerhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang 0001, Luziwei Leng   +8 more
openaire   +2 more sources

Electrically Coded Retinomorphic Spectrophotodetector

Advanced Materials, EarlyView.
Self‐powered retinomorphic pyro‐photodetector is demonstrated that avoids machine‐learning post‐processing and covers 365–940 nm. Electrostatic balancing of built‐in potential produces an electrical wavelength code, delivering <3 nm wavelength decoding accuracy with ∼46 µs response.
Mohit Kumar, Hyunmin Dang, Hyungtak Seo
wiley   +1 more source

Soft Skins With Reversible Thickness Morphing: Materials, Mechanisms, and Applications

Advanced Materials, EarlyView.
Evolution of electronic skin (e‐skin) technologies toward adaptive, multifunctional soft skins. Phase I highlights early rigid and discrete sensory interfaces. Phase II shows the transition toward flexible, stretchable, and large‐area e‐skin. Phase III captures the emergence of computational e‐skin.
Oliver Ozioko, Chiamaka Akah, Ravinder Dahiya   +2 more
wiley   +1 more source

Metal Oxide Nano‐Interface Boosting the Deep Ultraviolet Adjustable Noise‐Filtering In‐Sensor Computing

Advanced Materials, EarlyView.
We show that sol‐gel‐fractured indium–magnesium oxide combines deep‐ultraviolet responsivity, high carrier mobility, and an excellent memory dynamic range. This unique materials platform enables deep‐ultraviolet long‐afterglow light‐emitting devices with multifunctional integration.
Zhongshi Ju, Peng Li, Jingsong Yuan, Boyuan Yu, Bin Han, Yusheng Chen, Jiangang Ma, Haiyang Xu, Yichun Liu, Paolo Samorì   +9 more
wiley   +1 more source

A Spiking Neural Network with Attention and Residual Mechanisms for Compound Fault Detection

Machines
To address the challenges of severe multi-source coupling, easily masked spiking features, and limited selection of key responses in compound fault signals, this paper proposes a compound fault detection method based on a spiking attention residual ...
Yulong Xing, Kun Li, Xiaoshuai Li, Congcong Liu, Qi Wang, Cong Peng, Zisheng Wang   +6 more
doaj   +1 more source

Spiking Neural Network Pressure Sensor

Neural Computation
Abstract Von Neumann architecture requires information to be encoded as numerical values. For that reason, artificial neural networks running on computers require the data coming from sensors to be discretized. Other network architectures that more closely mimic biological neural networks (e.g., spiking neural networks) can be simulated ...
Michal Markiewicz, Ireneusz Brzozowski, Szymon Janusz   +2 more
openaire   +3 more sources

Noise‐Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency‐Selective Time‐Series Signal Encoding

Advanced Materials, EarlyView.
Memristors offer tunable resistance and intrinsic instability, making them promising tunable noise sources. We propose a spiking‐rate‐programmable probabilistic neuron using a Ru/TaOx/Pt memristor, where resistance‐dependent noise enables frequency‐selective encoding.
Do Hoon Kim, Seoeun Jang, Hakseung Rhee, Min Gu Lee, Daehee Kim, Taewook Go, Woon Hyung Cheong, Hanchan Song, Kyung Min Kim   +8 more
wiley   +1 more source