5 papers · 1 filter
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
Xiang He, Dongcheng Zhao, Yang Li +3
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion rese…
Similarity-based context aware continual learning for spiking neural networks
Bing Han, Feifei Zhao, Yang Li +3
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environmen…
Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient
Yang Li, Feifei Zhao, Dongcheng Zhao +1
Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none natur…
Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference
Yang Li, Xiang He, Qingqun Kong +1
Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference. However, the direct training of deep spiking neural netw…
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
Yang Li, Yinqian Sun, Xiang He +3
Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by…