Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model.

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities.

Trainable Spiking-YOLO for low-latency and high-performance object detection.

Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving good detection accuracy and high detection speed. To overcome the aforementioned challenges, we propose an end-to-end Trainable Spiking-YOLO (Tr-Spiking-YOLO) for low-latency and high-performance object detection.

Effects of mobile phone App-based continuing nursing care on self-efficacy, quality of life, and motor function of stroke patients in the community.

To explore the effects of mobile phone application (App)-based continuing nursing care on the self-efficacy (SE), quality of life (QOF), and motor function (MF) of stroke patients in the community. A total of 101 stroke patients in the community recruited in this study for retrospective analysis were divided into a control group (CG) and an observation group (OG) based on the means of intervention. In total, 50 patients in the CG received routine community health education, based on which a mobile phone App-based continuing nursing mode was applied to the 51 patients in the OG.

Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses.

Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking neurons involve only a single plasticity mechanism. Here, we propose a neural realistic reinforcement learning model that coordinates the plasticities of two types of synapses: stochastic and deterministic.