10 citations · 16 across the 20 of their papers we have counts for
20 papers
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…
Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism
Bing Han, Feifei Zhao, Yinqian Sun +2
Cognitive functions in current artificial intelligence networks are tied to the exponential increase in network scale, whereas the human brain can continuously learn hundreds of co…
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…
MTDP: A Modulated Transformer based Diffusion Policy Model
Qianhao Wang, Yinqian Sun, Enmeng Lu +2
Recent research on robot manipulation based on Behavior Cloning (BC) has made significant progress. By combining diffusion models with BC, diffusion policiy has been proposed, enab…
Autonomous Alignment with Human Value on Altruism through Considerate Self-imagination and Theory of Mind
Haibo Tong, Enmeng Lu, Yinqian Sun +4
With the widespread application of Artificial Intelligence (AI) in human society, enabling AI to autonomously align with human values has become a pressing issue to ensure its sust…
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…
Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model
Qianhao Wang, Yinqian Sun, Enmeng Lu +2
Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification…
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…
A Brain-inspired Theory of Collective Mind Model for Efficient Social Cooperation
Zhuoya Zhao, Feifei Zhao, Shiwen Wang +2
Social intelligence manifests the capability, often referred to as the Theory of Mind (ToM), to discern others' behavioral intentions, beliefs, and other mental states. ToM is espe…
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…
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…
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…
Brain-inspired bodily self-perception model for robot rubber hand illusion
Yuxuan Zhao, Enmeng Lu, Yi Zeng
At the core of bodily self-consciousness is the perception of the ownership of one's body. Recent efforts to gain a deeper understanding of the mechanisms behind the brain's encodi…
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…
A Brain-inspired Memory Transformation based Differentiable Neural Computer for Reasoning-based Question Answering
Yao Liang, Hongjian Fang, Yi Zeng +1
Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural…
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…