115 citations · 156 across the 9 of their papers we have counts for
9 papers
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
Bing Han, Feifei Zhao, Wenxuan Pan +4
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms…
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +2
Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing conti…
Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
Wenxuan Pan, Feifei Zhao, Yi Zeng +1
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spi…
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +1
Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing net…
Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +1
Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity…
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing
Bing Han, Cheng Wang, Kaushik Roy
Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/v…