10 citations · 16 across the 20 of their papers we have counts for
12 papers · 1 filter
TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers
Sicheng Shen, Mingyang Lv, Bing Han +4
In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence mode…
Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
Yinqian Sun, Feifei Zhao, Mingyang Lv +1
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision…
Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Bing Han +2
By exploiting discrete signal processing and simulating brain neuron communication, Spiking Neural Networks (SNNs) offer a low-energy alternative to Artificial Neural Networks (ANN…
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…
Brain-inspired Evolutionary Architectures for Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Zhuoya Zhao +1
The complex and unique neural network topology of the human brain formed through natural evolution enables it to perform multiple cognitive functions simultaneously. Automated evol…
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…
Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
Guobin Shen, Dongcheng Zhao, Yiting Dong +3
The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our e…
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Guobin Shen +1
Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficiency with discrete signal processing but also for their natural s…
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…
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning
Yinqian Sun, Feifei Zhao, Zhuoya Zhao +1
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorpor…
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…