JiaHong Zhang's HomePage

Enhancing Robustness of Spiking Neural Networks Through Retina-Like Coding and Memory-Based Neurons
J Zhang, K Wang, M Yao, H Xu, P Zhou, B Xu, G Li
Neural Networks, 2025
[Arxiv] [Paper] [Project]

Enabling Scale and Rotation Invariance in Convolutional Neural Networks with Retina Like Transformation
J Zhang, Guoqi Li, Qiaoyi Su, Lihong Cao, Yonghong Tian, Bo Xu
Neural Networks, 2025
[Arxiv] [Paper] [Project]

SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network
K Wang, J Zhang, Y Ren, M Yao, D Shang, B Xu, G Li
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024
[Arxiv] [Paper] [Project]

A Brain-inspired Method for Occluded 3D Object Recognition
Zining Wan*, Jiahong Zhang*, Lihong Cao.
International Joint Conference on Neural Networks (IJCNN), 2024
[Arxiv] [Paper] [Project]

Considering Image Information and Self-similarity: A Compositional Denoising Network
Jiahong Zhang, Yonggui Zhu, Wenshu Yu, Jingning Ma.
Sensors, 2023
[Arxiv] [Paper] [Project]

Extracting the Brain-like Representation by an Improved Self-Organizing Map for Image Classification
Zhang J, Cao L, Zhang M, Fu W.
ICASSP, 2023
[Paper] [Project] [Bibtex]

		@inproceedings{zhang2023extracting,
			title={Extracting the Brain-Like Representation by an Improved Self-Organizing Map for Image Classification},
			author={Zhang, Jiahong and Cao, Lihong and Zhang, Moning and Fu, Wenlong},
			booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
			pages={1--5},
			year={2023},
			organization={IEEE}
		  }

BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition
Zhang J, Cao L, Lai Q, Li B, Qin Y.
AAAI, 2023
[Paper] [Bibtex]

		@article{zhang2023bifrnet,
			title={BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition},
			author={Zhang, Jiahong and Cao, Lihong and Lai, Qiuxia and Li, Binyao and Qin, Yunxiao},
			journal={arXiv preprint arXiv:2303.01309},
			year={2023}
		  }

A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
Zhang J, Zhu Y, Li W, et al.
PRICAI, 2022
[Paper] [Project] [Bibtex]

			@article{zhang2022multi,
				title={A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising},
				author={Zhang, Jiahong and Qu, Meijun and Wang, Ye and Cao, Lihong},
				journal={arXiv preprint arXiv:2204.12736},
				year={2022}
			}

Retrospective memory integration accompanies reconfiguration of neural cell assemblies[J]
Ye Wang, Yaling Deng, Lihong Cao, Jiahong Zhang, Lei Yang
Hippocampus, 2022
[Paper] [ArXiv] [Project] [Bibtex]

	@article{https://doi.org/10.1002/hipo.23399,
		author = {Wang, Ye and Deng, Yaling and Cao, Lihong and Zhang, Jiahong and Yang, Lei},
		title = {Retrospective memory integration accompanies reconfiguration of neural cell assemblies},
		journal = {Hippocampus},
		volume = {32},
		number = {3},
		pages = {179-192},
		keywords = {associative inference, associative memory, cell assembly, recurrent spiking neural network, retrospective memory integration},
		doi = {https://doi.org/10.1002/hipo.23399},
		url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hipo.23399},
		eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hipo.23399},
		abstract = {Abstract Memory is a dynamic process that is based on and can be altered by experiences. Integrating memories of multiple experiences (memory integration) is the basis of flexible and complex decision-making. However, the mechanism of memory integration in neural networks of the brain remains poorly understood. In this study, we built a recurrent spiking network model and investigated the neural mechanism of memory integration before a decision is made (retrospective memory integration) at the neural circuit level. Our simulations suggest that retrospective memory integration accompanies reconfiguration of neural cell assemblies. Additionally, partially blocking neural network plasticity leads to failure of memory integration. These findings can potentially guide the experimental investigation of the neural mechanism of retrospective memory integration and serve as the basis for developing new artificial intelligence algorithms.},
		year = {2022}
		}

NHNet: A non‐local hierarchical network for image denoising
Zhang J, Cao L, Wang T, et al.
IET Image Processing, 2022
[Paper] [Project] [Bibtex]

	@article{zhang2022nhnet,
		title={NHNet: A non-local hierarchical network for image denoising},
		author={Zhang, Jiahong and Cao, Lihong and Wang, Tian and Fu, Wenlong and Shen, Weiheng},
		journal={IET Image Processing},
		volume={16},
		number={9},
		pages={2446--2456},
		year={2022},
		publisher={Wiley Online Library}
	  }

DRNet: A deep neural network with multi-layer residual blocks improves image denoising
Zhang J, Zhu Y, Li W, et al.
IEEE Access, 2021
[Paper] [Project] [Bibtex]

	@ARTICLE{9444389,  author={Zhang, Jiahong and Zhu, Yonggui and Li, Wenyi and Fu, Wenlong and Cao, Lihong},  journal={IEEE Access},   title={DRNet: A Deep Neural Network With Multi-Layer Residual Blocks Improves Image Denoising},   year={2021},  volume={9},  number={},  pages={79936-79946},  doi={10.1109/ACCESS.2021.3084951}}