8 citations · 12 across the 4 of their papers we have counts for
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…
Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes
Yang Li, Yi Zeng
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers. While the training of spiking neural networks is still…
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
Dongcheng Zhao, Yi Zeng, Yang Li
Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. Due to the non-differentiable character…
BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons
Yang Li, Yi Zeng, Dongcheng Zhao
The spiking neural network (SNN) computes and communicates information through discrete binary events. It is considered more biologically plausible and more energy-efficient than a…