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Patterns (New York, N.Y.)|June 27, 2022
Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networksGuobin Shen, Dongcheng Zhao, Yi ZengNeural Networks : the Official Journal of the International Neural Network Society|November 21, 2023
Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural NetworksGuobin Shen, Dongcheng Zhao, Yi ZengProceedings of the National Academy of Sciences of the United States of America|September 20, 2023
Brain-inspired neural circuit evolution for spiking neural networksGuobin Shen, Dongcheng Zhao, Yiting Dong, et al.Neural Networks : the Official Journal of the International Neural Network Society|July 21, 2022
BackEISNN: A deep spiking neural network with adaptive self-feedback and balanced excitatory-inhibitory neuronsDongcheng Zhao, Yi Zeng, Yang LiFrontiers in Neuroscience|October 31, 2022
BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neuronsYang Li, Dongcheng Zhao, Yi ZengNeural Networks : the Official Journal of the International Neural Network Society|June 21, 2024
Spiking generative adversarial network with attention scoring decodingLinghao Feng, Dongcheng Zhao, Yi ZengNeural Networks : the Official Journal of the International Neural Network Society|July 16, 2024
Directly training temporal Spiking Neural Network with sparse surrogate gradientYang Li, Feifei Zhao, Dongcheng Zhao, et al.Neural Networks : the Official Journal of the International Neural Network Society|May 6, 2026
Biologically inspired spiking diffusion model with adaptive lateral selection mechanismLinghao Feng, Dongcheng Zhao, Sicheng Shen, et al.Scientific Data|December 2, 2022
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningYang Li, Yiting Dong, Dongcheng Zhao, et al.Neural Networks : the Official Journal of the International Neural Network Society|July 7, 2023
An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connectionsYiting Dong, Dongcheng Zhao, Yang Li, et al.Pageof 191