13 citations · 53 across the 14 of their papers we have counts for
14 papers
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Chenxiang Ma, Xinyi Chen, Yanchen Li +7
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses…
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
Shibo Zhou, Bo Yang, Mengwen Yuan +4
Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable a…
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
Yangfan Hu, Qian Zheng, Guoqi Li +2
Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress in fields such as computer vision, speech recognition, and natural language processing.…
Context Gating in Spiking Neural Networks: Achieving Lifelong Learning through Integration of Local and Global Plasticity
Jiangrong Shen, Wenyao Ni, Qi Xu +2
Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spikin…
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Qi Xu, Yuyuan Gao, Jiangrong Shen +4
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from…
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
De Ma, Xiaofei Jin, Shichun Sun +8
Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-tem…
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
Jiangrong Shen, Qi Xu, Jian K. Liu +3
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Qi Xu, Yaxin Li, Xuanye Fang +4
Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current s…
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Qi Xu, Yaxin Li, Jiangrong Shen +3
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, clo…
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Lang Feng, Qianhui Liu, Huajin Tang +2
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low e…
SPAIC: A Spike-based Artificial Intelligence Computing Framework
Chaofei Hong, Mengwen Yuan, Mengxiao Zhang +6
Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscien…
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
Dong Xing, Qian Zheng, Qianhui Liu +1
Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, mo…
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Qianhui Liu, Haibo Ruan, Dong Xing +2
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditi…
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
Qianhui Liu, Gang Pan, Haibo Ruan +3
This paper proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (Mu…