Explore the vast range of titles available.

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive‐like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive‐like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive‐like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive‐like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive‐like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive‐like devices, and lay a foundation for academic research and commercial applications. With the unique characteristic, photonic memristive and memristive‐like devices significantly facilitate the construction of neuromorphic computing and artificial visual systems. This review elaborates on the resistive switching mechanisms, active layer materials, and potential applications in the scientific and industrial of photonic memristive and memristive‐like devices, aiming to provide a systematic and comprehensive insight into its development.

The human brain performs computations via a highly interconnected network of neurons. Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems, bioinspired nanofluidic iontronics has been proposed and gradually engineered to overcome the limitations of the conventional electron-based von Neumann architecture, which shows the promising potential to enable efficient brain-like computing. Anomalous and tunable nanofluidic ion transport behaviors and spatial confinement show promising controllability of charge carriers, and a wide range of structural and chemical modification paves new ways for realizing brain-like functions. Herein, a comprehensive framework of mechanisms and design strategy is summarized to enable the rational design of nanofluidic systems and facilitate the further development of bioinspired nanofluidic iontronics. This review provides recent advances and prospects of the bioinspired nanofluidic iontronics, including ion-based brain computing, comprehension of intrinsic mechanisms, design of artificial nanochannels, and the latest artificial neuromorphic functions devices. Furthermore, the challenges and opportunities of bioinspired nanofluidic iontronics in the pioneering and interdisciplinary research fields are proposed, including brain–computer interfaces and artificial neurons.

Spiking Neural Networks (SNNs) possess excellent computational energy efficiency and biological credibility. Among them, Spiking Convolutional Neural Networks (SCNNs) have significantly improved performance, demonstrating promising applications in low-power and brain-like computing. To achieve hardware acceleration for SCNNs, we propose an efficient FPGA accelerator architecture with multi-structure compatibility. This architecture supports both traditional convolutional and residual topologies, and can be adapted to diverse requirements from small networks to complex networks. This architecture uses a clock-driven scheme to perform convolution and neuron updates based on the spike-encoded image at each timestep. Through hierarchical pipelining and channel parallelization strategies, the computation speed of SCNNs is increased. To address the issue of current accelerators only supporting simple network, this architecture combines configuration and scheduling methods, including grouped reuse computation and line-by-line multi-timestep computation to accelerate deep networks with lots of channels and large feature map sizes. Based on the proposed accelerator architecture, we evaluated two scales of networks, named small-scale LeNet and deep residual SCNN, for object detection. Experiments show that the proposed accelerator achieves a maximum recognition speed of 1, 605 frames/s at a 100 MHz clock for the LeNet network, consuming only 0.65 mJ per image. Furthermore, the accelerator, combined with the proposed configuration and scheduling methods, achieves acceleration for each residual module in the deep residual SCNN, reaching a processing speed of 2.59 times that of the CPU with a power consumption of only 16.77% of the CPU. This demonstrates that the proposed accelerator architecture can achieve higher energy efficiency, compatibility, and wider applicability.

Aiming at the problem that the fault samples of UHV converter equipment are few and cannot effectively support the intelligent operation and inspection of the equipment, this paper proposes a brain-like learning sample spatio-temporal correlation generation technique for the operation and inspection of UHV converter equipment. In this technique, GPNN fuses the temporal evolution law and similarity of nearby samples to intercept typical fault samples and then combines the SNNs model of brain-like computing to construct an intelligent diagnosis model for UHV converter equipment. The improved K-SVD dictionary learning algorithm is used to extract the time-domain features of the UHV converter faults, combined with the empirical wavelet singular entropy to obtain the frequency-domain features, and the KPCA algorithm is used to fuse the multiscale time-frequency features to obtain the multiscale spatial and temporal features of the faults of UHV converter equipment. The GPNN model for generating multi-scale spatio-temporal sequence fault samples is constructed by combining GAN with the nearest neighbor interpolation algorithm. The fault samples generated by the GPNN model are used as inputs and combined with the SNNs model for intelligent diagnosis of UHV converter equipment faults. The consistency between the fault samples generated by the GPNN model and the actual samples reaches more than 90.57%, the accuracy of the brain-like intelligent fault recognition model reaches up to 98.06%, and its training time is only 37.14 seconds. Learning the multi-scale features of the samples through the GPNN model, combined with brain-like computing technology, can support the training of brain-like models for health assessment, fault diagnosis, and trend prediction of UHV converter equipment.

Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance and practicability compared with the earlier research results, and are continuously producing significant results. Although there are already many pieces of literature on SNNs, there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results. Starting from this issue, this paper elaborates on SNNs along the complete usage process of SNNs including network construction, data processing, model training, development, and deployment, aiming to provide more comprehensive and practical guidance to promote the development of SNNs. Therefore, the connotation and development status of SNN computing is reviewed systematically and comprehensively from four aspects: composition structure, data set, learning algorithm, software/hardware development platform. Then the development characteristics of SNNs in intelligent computing are summarized, the current challenges of SNNs are discussed and the future development directions are also prospected. Our research shows that in the fields of machine learning and intelligent computing, SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks. The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited. And the development of programming and deployment tools has lowered the threshold for the use of SNNs. SNNs show a broad development prospect for brain-like computing.

Brain-machine interfaces (BMI) have developed rapidly in recent years, but still face critical issues such as accuracy and stability. Ideally, a BMI system would be an implantable neuroprosthesis that would be tightly connected and integrated into the brain. However, the heterogeneity of brains and machines hinders deep fusion between the two. Neuromorphic computing models, which mimic the structure and mechanism of biological nervous systems, present a promising approach to developing high-performance neuroprosthesis. The biologically plausible property of neuromorphic models enables homogeneous information representation and computation in the form of discrete spikes between the brain and the machine, promoting deep brain-machine fusion and bringing new breakthroughs for high-performance and long-term usable BMI systems. Furthermore, neuromorphic models can be computed at ultra-low energy costs and thus are suitable for brain-implantable neuroprosthesis devices. The intersection of neuromorphic computing and BMI has great potential to lead the development of reliable, low-power implantable BMI devices and advance the development and application of BMI.

With the development of technology, Moore's law will come to an end, scientists try to find a new way out in brain-like computing. But we still know very little about how the brain works. At the present stage of research, brain-like models are all structured to mimic the brain in order to achieve some of the brain's functions, and then continue to improve the theories and models. This paper summarizes the important progress and status of brain-like computing, summarizes the generally accepted and feasible brain-like computing models, introduces, analyzes and compares the more mature brain-like computing chips, outlines the attempts and challenges of brain-like computing applications at this stage, and looks forward to the future development of brain-like computing. It is hoped that the summarized results will help relevant researchers and practitioners to quickly grasp the research progress in the field of brain-like computing and acquire the application methods and related knowledge in this field.

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry.

Spiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural systems work by simulating the transmission of information through discrete spiking signals between neurons. Influenced by the great potential shown by the attention mechanism in convolutional neural networks, Therefore, we propose a Spiking Attention Neural Network (Sa-SNN). The network includes a novel Spiking-Efficient Channel Attention (SECA) module that adopts a local cross-channel interaction strategy without dimensionality reduction, which can be achieved by one-dimensional convolution. It is implemented by convolution, which involves a small number of model parameters but provides a significant performance improvement for the network. The design of local inter-channel interactions through adaptive convolutional kernel sizes, rather than global dependencies, allows the network to focus more on the selection of important features, reduces the impact of redundant features, and improves the network’s recognition and generalisation capabilities. To investigate the effect of this structure on the network, we conducted a series of experiments. Experimental results show that Sa-SNN can perform image classification tasks more accurately. Our network achieved 99.61%, 99.61%, 94.13%, and 99.63% on the MNIST, Fashion-MNIST, N-MNIST datasets, respectively, and Sa-SNN performed well in terms of accuracy compared with mainstream SNNs.