| An attention-driven two-stage clustering method for unsupervised person re-identification Z Ji, X Zou, X Lin, X Liu, T Huang, S Wu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 65 | 2020 |
| Unsupervised few-shot feature learning via self-supervised training Z Ji, X Zou, T Huang, S Wu Frontiers in computational neuroscience 14, 83, 2020 | 47 | 2020 |
| A brain-inspired computational model for spatio-temporal information processing X Lin, X Zou, Z Ji, T Huang, S Wu, Y Mi Neural Networks 143, 74-87, 2021 | 23 | 2021 |
| Firing rate adaptation in continuous attractor neural networks accounts for theta phase shift of hippocampal place cells T Chu, Z Ji, J Zuo, Y Mi, W Zhang, T Huang, D Bush, N Burgess, S Wu bioRxiv 3 (4), 5, 2022 | 17* | 2022 |
| Noisy adaptation generates lévy flights in attractor neural networks X Dong, T Chu, T Huang, Z Ji, S Wu Advances in Neural Information Processing Systems 34, 16791-16804, 2021 | 14 | 2021 |
| Overestimation in angular path integration precedes Alzheimer’s dementia A Castegnaro, Z Ji, K Rudzka, D Chan, N Burgess Current Biology 33 (21), 4650-4661. e7, 2023 | 13 | 2023 |
| Closing the loop: tracking and perturbing behaviour of individuals in a group in real-time MJ Rasch, A Shi, Z Ji bioRxiv, 071308, 2016 | 13 | 2016 |
| Entorhinal‐based path integration selectively predicts midlife risk of Alzheimer's disease C Newton, M Pope, C Rua, R Henson, Z Ji, N Burgess, CT Rodgers, ... Alzheimer's & Dementia 20 (4), 2779-2793, 2024 | 11 | 2024 |
| Path integration selectively predicts midlife risk of Alzheimer’s disease C Newton, M Pope, C Rua, R Henson, Z Ji, N Burgess, CT Rodgers, ... bioRxiv, 2023 | 8 | 2023 |
| Adaptation accelerating sampling-based bayesian inference in attractor neural networks X Dong, Z Ji, T Chu, T Huang, W Zhang, S Wu Advances in Neural Information Processing Systems 35, 21534-21547, 2022 | 8 | 2022 |
| Neural feedback facilitates rough-to-fine information retrieval X Liu, X Zou, Z Ji, G Tian, Y Mi, T Huang, KYM Wong, S Wu Neural Networks 151, 349-364, 2022 | 8 | 2022 |
| Learning a continuous attractor neural network from real images X Zou, Z Ji, X Liu, Y Mi, KYM Wong, S Wu Neural Information Processing: 24th International Conference, ICONIP 2017 …, 2017 | 8 | 2017 |
| A just-in-time compilation approach for neural dynamics simulation C Wang, Y Jiang, X Liu, X Lin, X Zou, Z Ji, S Wu Neural Information Processing: 28th International Conference, ICONIP 2021 …, 2021 | 7 | 2021 |
| Oscillatory tracking of continuous attractor neural networks account for phase precession and procession of hippocampal place cells T Chu, Z Ji, J Zuo, W Zhang, T Huang, Y Mi, S Wu Advances in Neural Information Processing Systems 35, 33159-33172, 2022 | 4 | 2022 |
| Spatiotemporal information processing with a reservoir decision-making network Y Mi, X Lin, X Zou, Z Ji, T Huang, S Wu arXiv preprint arXiv:1907.12071, 2019 | 4 | 2019 |
| Push-pull feedback implements hierarchical information retrieval efficiently X Liu, X Zou, Z Ji, G Tian, Y Mi, T Huang, KY Wong, S Wu Advances in Neural Information Processing Systems 32, 2019 | 3 | 2019 |
| Neural information processing in hierarchical prototypical networks Z Ji, X Zou, X Liu, T Huang, Y Mi, S Wu Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | 3 | 2018 |
| BrainScale: Enabling scalable online learning in spiking neural networks C Wang, X Dong, J Jiang, Z Ji, X Liu, S Wu bioRxiv, 2024.09. 24.614728, 2024 | 2 | 2024 |
| Visual information processing through the interplay between fine and coarse signal pathways X Zou, Z Ji, T Zhang, T Huang, S Wu Neural Networks 166, 692-703, 2023 | 2 | 2023 |
| A systems model of alternating theta sweeps via firing rate adaptation Z Ji, T Chu, S Wu, N Burgess bioRxiv, 2024 | 1 | 2024 |
Si WuPeking UniversityBestätigte E-Mail-Adresse bei pku.edu.cn
