33 citations · 70 across the 22 of their papers we have counts for
24 papers
Rethinking Pairwise Token Interaction in Spiking Transformers
Sicheng Shen, Dongcheng Zhao, Zhiyuan Li +5
Spiking Transformers inherit token interaction mechanisms from conventional Transformers, yet their sparse binary representations fundamentally alter how token-to-token communicati…
Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer
Dongcheng Zhao, Sicheng Shen, Zhenyu Yang +6
Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary query and key representations map continuous similarities to sparse an…
UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces
Binjie Hong, Rui Xiong, Liyuan Han +1
Modeling invasive neural spike data is fundamental to advancing high-performance brain-computer interfaces (BCIs). However, existing approaches face critical challenges, including…
TaiBai: A fully programmable brain-inspired processor with topology-aware efficiency
Qianpeng Li, Yu Song, Xin Liu +6
Brain-inspired computing has emerged as a promising paradigm to overcome the energy-efficiency limitations of conventional intelligent systems by emulating the brain's partitioned…
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding
Yu Song, Liyuan Han, Bo Xu +1
Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Netwo…
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks
Tianyu Zheng, Liyuan Han, Tielin Zhang
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational ef…
Adaptive Runge-Kutta Dynamics for Spatiotemporal Prediction
Xuanle Zhao, Yue Sun, Ziyi Wang +2
Spatiotemporal prediction is important in solving natural problems and processing video frames, especially in weather forecasting and human action recognition. Recent advances atte…
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
Duzhen Zhang, Qingyu Wang, Tielin Zhang +1
The success of Deep Reinforcement Learning (DRL) is largely attributed to utilizing Artificial Neural Networks (ANNs) as function approximators. Recent advances in neuroscience hav…
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification
Qingyu Wang, Duzhen Zhang, Tilelin Zhang +1
Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention…
Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation
Zifan Ning, Chaojin Chen, Xiang Cheng +3
As next-generation implantable brain-machine interfaces become pervasive on edge device, incrementally learning new tasks in bio-plasticity ways is urgently demanded for Neuromorph…
Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
Xuanle Zhao, Yue Sun, Tielin Zhang +1
Neural operators improve conventional neural networks by expanding their capabilities of functional mappings between different function spaces to solve partial differential equatio…
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
Xuanle Zhao, Duzhen Zhang, Liyuan Han +2
Neural ordinary differential equations (ODEs) are widely recognized as the standard for modeling physical mechanisms, which help to perform approximate inference in unknown physica…
Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms
Qingyu Wang, Duzhen Zhang, Tielin Zhang +1
By integrating the self-attention capability and the biological properties of Spiking Neural Networks (SNNs), Spikformer applies the flourishing Transformer architecture to SNNs de…
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
Jinye Qu, Zeyu Gao, Tielin Zhang +3
Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have…
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Xiyun Li, Ziyi Ni, Jingqing Ruan +4
Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has…
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition
Minglun Han, Qingyu Wang, Tielin Zhang +3
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks. However, the LIF neuron is still…
Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
Duzhen Zhang, Tielin Zhang, Shuncheng Jia +2
Learning from interaction is the primary way that biological agents acquire knowledge about their environment and themselves. Modern deep reinforcement learning (DRL) explores a co…
Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect
Shuncheng Jia, Tielin Zhang, Ruichen Zuo +1
Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are conside…
Recent Advances and New Frontiers in Spiking Neural Networks
Duzhen Zhang, Shuncheng Jia, Qingyu Wang
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding met…
Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration
Shuncheng Jia, Ruichen Zuo, Tielin Zhang +2
Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the…
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
Duzhen Zhang, Tielin Zhang, Shuncheng Jia +2
With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to…
Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning
Xiang Cheng, Tielin Zhang, Shuncheng Jia +1
Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artifi…
Quantum Superposition Inspired Spiking Neural Network
Yinqian Sun, Yi Zeng, Tielin Zhang
Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed co…
Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation
Tielin Zhang, Shuncheng Jia, Xiang Cheng +1
Spiking Neural Networks (SNNs) contain more biologically realistic structures and biologically-inspired learning principles than those in standard Artificial Neural Networks (ANNs)…