| Cortical circuit dynamics are homeostatically tuned to criticality in vivo Z Ma, GG Turrigiano, R Wessel, KB Hengen Neuron 104 (4), 655-664. e4, 2019 | 189 | 2019 |
| CuInSe 2 ultrathin nanoplatelets: novel self-sacrificial template-directed synthesis and application for flexible photodetectors W Bi, M Zhou, Z Ma, H Zhang, J Yu, Y Xie Chemical communications 48 (73), 9162-9164, 2012 | 75 | 2012 |
| Neocortical activity is stimulus-and scale-invariant Y Karimipanah, Z Ma, JK Miller, R Yuste, R Wessel PloS one 12 (5), e0177396, 2017 | 35 | 2017 |
| Spikingformer: Spike-driven residual learning for transformer-based spiking neural network C Zhou, L Yu, Z Zhou, Z Ma, H Zhang, H Zhou, Y Tian arXiv preprint arXiv:2304.11954, 2023 | 28 | 2023 |
| State transition of dendritic spines improves learning of sparse spiking neural networks Y Chen, Z Yu, W Fang, Z Ma, T Huang, Y Tian International Conference on Machine Learning, 3701-3715, 2022 | 27 | 2022 |
| Stability of motor cortex network states during learning-associated neural reorganizations Z Ma, H Liu, T Komiyama, R Wessel Journal of Neurophysiology 124 (5), 1327-1342, 2020 | 18 | 2020 |
| Neuron-based spiking transmission and reasoning network for robust image-text retrieval W Li, Z Ma, LJ Deng, X Fan, Y Tian IEEE Transactions on Circuits and Systems for Video Technology, 2022 | 15 | 2022 |
| Parallel spiking neurons with high efficiency and ability to learn long-term dependencies W Fang, Z Yu, Z Zhou, D Chen, Y Chen, Z Ma, T Masquelier, Y Tian Advances in Neural Information Processing Systems 36, 2024 | 13* | 2024 |
| A unified framework for soft threshold pruning Y Chen, Z Ma, W Fang, X Zheng, Z Yu, Y Tian arXiv preprint arXiv:2302.13019, 2023 | 13 | 2023 |
| Criticality predicts maximum irregularity in recurrent networks of excitatory nodes Y Karimipanah, Z Ma, R Wessel PLoS One 12 (8), e0182501, 2017 | 10* | 2017 |
| Deep spiking neural networks with high representation similarity model visual pathways of macaque and mouse L Huang, Z Ma, L Yu, H Zhou, Y Tian Proceedings of the AAAI Conference on Artificial Intelligence 37 (1), 31-39, 2023 | 6 | 2023 |
| Enhancing the performance of transformer-based spiking neural networks by improved downsampling with precise gradient backpropagation C Zhou, H Zhang, Z Zhou, L Yu, Z Ma, H Zhou, X Fan, Y Tian arXiv preprint arXiv:2305.05954, 2023 | 6 | 2023 |
| Auto-Spikformer: Spikformer Architecture Search K Che, Z Zhou, Z Ma, W Fang, Y Chen, S Shen, L Yuan, Y Tian arXiv preprint arXiv:2306.00807, 2023 | 5 | 2023 |
| Motion-Decoupled Spiking Transformer for Audio-Visual Zero-Shot Learning W Li, XL Zhao, Z Ma, X Wang, X Fan, Y Tian Proceedings of the 31st ACM International Conference on Multimedia, 3994-4002, 2023 | 3 | 2023 |
| Reservoir Computing Transformer for Image-Text Retrieval W Li, Z Ma, LJ Deng, P Wang, J Shi, X Fan Proceedings of the 31st ACM International Conference on Multimedia, 5605-5613, 2023 | 2 | 2023 |
| Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network J Zhang, L Huang, Z Ma, H Zhou Cognitive Neurodynamics, 1-12, 2023 | 2 | 2023 |
| Modulation of neuronal activity and saccades at theta rhythm during visual search in non-human primates J Xie, T Yan, J Zhang, Z Ma, H Zhou Neuroscience Bulletin 38 (10), 1183-1198, 2022 | 2 | 2022 |
| Visual attention in the fovea and the periphery during visual search J Zhang, X Zhu, S Wang, Z Ma, H Esteky, Y Tian, R Desimone, H Zhou bioRxiv, 2021.11. 22.469359, 2021 | 2 | 2021 |
| QKFormer: Hierarchical Spiking Transformer using QK Attention C Zhou, H Zhang, Z Zhou, L Yu, L Huang, X Fan, L Yuan, Z Ma, H Zhou, ... arXiv preprint arXiv:2403.16552, 2024 | 1 | 2024 |
| SGLFormer: Spiking Global-Local-Fusion Transformer with High Performance H Zhang, C Zhou, L Yu, L Huang, Z Ma, X Fan, H Zhou, Y Tian Frontiers in Neuroscience 18, 1371290, 2024 | 1 | 2024 |
