1.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Chinese Academy of SciencesCN72 papers
- Chinese Academy of SciencesCN70 papers
- Institute of AutomationCN57 papers
- Beijing Academy of Artificial IntelligenceCN20 papers
- Institute of Computing TechnologyCN11 papers
- Shandong Institute of AutomationCN11 papers
- University of Science and Technology of ChinaCN5 papers
- Peng Cheng LaboratoryCN4 papers
- Tsinghua UniversityCN4 papers
- Horizon Robotics (China)3 papers
- Ningbo Institute of Industrial TechnologyCN3 papers
- Tongji UniversityCN3 papers
6 papers · 1 filter
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…
FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization
Jindong Li, Guobin Shen, Dongcheng Zhao +2
Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high energy efficiency. With the introduction of the backpropagation algori…
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
Yi Zeng, Dongcheng Zhao, Feifei Zhao +16
Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological…
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Yang Li, Yiting Dong, Dongcheng Zhao +1
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet diffi…
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…
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
Yunzhe Hao, Xuhui Huang, Meng Dong +1
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are n…