Direct training high-performance deep spiking neural networks: a review of theories and methods.

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes.

SGLFormer: Spiking Global-Local-Fusion Transformer with high performance.

Introduction: Spiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.

Methods: Recognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs.

Communication speeds up but impairs the consensus decision in a dyadic colour estimation task.

Communication plays an important role in consensus decision-making which pervades our daily life. However, the exact role of communication in consensus formation is not clear. Here, to study the effects of communication on consensus formation, we designed a dyadic colour estimation task, where a pair of isolated participants repeatedly estimated the colours of discs until they reached a consensus or completed eight estimations, either with or without communication.

Flexible superior electrode architectures based on three-dimensional porous spinous α-Fe2O3 with a high performance as a supercapacitor.

Flexible supercapacitors have recently attracted increasing attention as they show unique promising advantages, such as flexibility and shape diversity, and they are light-weight and so on. Herein, we designed a series of 3D porous spinous iron oxide materials synthesized on a thin iron plate through a facile method under mild conditions. The unique nanostructural features endow them with excellent electrochemical performance.

Effective electrocatalysis based on Ag2O nanowire arrays supported on a copper substrate.

Silver oxide nanowire arrays (Ag2O NWAs) were first synthesized on a copper (Cu) rod by a simple and facile wet-chemistry approach without using any surfactants. The as-synthesized Ag2O NWA/Cu rod not only can be used as an integrated electrode (called a Ag2O NWA/CRIE) to detect hydrazine (HZ) but also can serve as the catalyst layer for a direct HZ fuel cell. The current density of HZ oxidation on Ag2O NWA (94.

High electrochemical performance based on ultrathin porous CuO nanobelts grown on Cu substrate as integrated electrode.

A facile and low-cost approach has been developed to fabricate porous CuO nanobelts directly grown on a Cu substrate. The as-prepared CuO samples can be directly used as integrated electrodes for lithium-ion batteries and pseudo-supercapacitors without the addition of other ancillary materials such as carbon black or a binder to enhance electrode conductivity and cycling stability. The unique nanostructural features endow them with excellent electrochemical performance as demonstrated by high capacities of 640 mA h g(-1) after 100 cycles at 0.