3 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…