14 citations · 14 across the 5 of their papers we have counts for
4 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…
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…
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Yang Li, Yiting Dong, Dongcheng Zhao +1
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet diffi…