Publications
Neuroscience and computational neuroscience
Yu, L., Yang, Z., Zang, Y*. Balancing inhibition and sparsity for stable, accurate cerebellar learning. Under review in Journal of Neuroscience. *correspondence author. https://doi.org/10.64898/2026.04.09.717611
Yu, L., Yang, Z., Yang, Z., Zang, Y*. Adaptive cerebellar feedback shapes cortical dynamics to mitigate memory interference during multi-task learning. Submitted. *correspondence author.
Li, Y., Sun, X., Zang, Y*. Neuronal XOR computation requires temporally activated synaptic information. Submitted to PLOS Computational Biology. *correspondence author.
Zhang, Y., Zhang, J., Zang, Y*. Cerebellar and cerebellum-like computations for novelty: progress and conceptual models for future directions. Under revision in CSIAM Transactions on Life Sciences. *correspondence author.
Shao, W., Meng, W., Ma, Z., Zhang, X., Qiao, G., Yin, W., Wang, R., Shao, Q., Wang, Y., Chen, C*., Zang, Y., Li, X. Empowering bio-reservoir computing: dynamic and structural enhancements for superior spatiotemporal pattern recognition. Under revision in Neural Networks. *co-correspondence author.
Bao, Y., Yu, L., Lv, L., Yang, Z., Zang, Y*. Cerebellar microcircuits enable robust evidence-based decisions through cortico–cerebellar coupling. Accepted in PNAS. *correspondence author. https://doi.org/10.64898/2026.04.26.720835
Yu L., Yang Z., Zang Y (2026). Noises and astrocyte regulation orchestrate heterogeneous neuronal firing. Nonlinear Dynamics, 114 (3), 170.
Li, H., Yu, L., Yu, Q., Zang, Y* (2025). Seemingly redundant modules enhance robust odor learning in fruit flies. NeurIPS. *correspondence author.
Shao, W., Shao, Q., … Zang, Y., Li, X (2025). Repetitive training enhances the pattern recognition capability of cultured neural networks. PLOS Computational Biology, 21(4) e1013043. *co-correspondence author.
Zang, Y*., Marder, E., Marom, S (2023). Sodium channel slow inactivation normalizes firing in axons with uneven conductance distributions. Current Biology, 33 (9), 1818-1824. *correspondence author.
Zang, Y., Marder, E (2023). Neuronal morphology enhances robustness to perturbations of channel densities. PNAS, 120 (8): e2219049120.
Zang, Y., De Schutter, E (2023). Recent data on the cerebellum require new models and theories. Current Opinion in Neurobiology, 82, 102765. *correspondence author.
Zang, Y., Marder, E (2022). Reply to Kotler et al.: Changing ion concentrations in conductance-based models. PNAS, 119 (12): e2121944119.
Zang, Y., Marder, E (2021). Interactions among diameter, myelination, and the Na/K pump affect axonal resilience to high-frequency spiking. PNAS, 118 (32): e2105795118.
Zang, Y*., De Schutter, E (2021). The cellular electrophysiological properties underlying multiplexed coding in Purkinje cells. The Journal of Neuroscience, 41(9): 1850–1863. *Correspondence author.
Zang, Y., Hong, S, De Schutter, E (2020). Firing rate-dependent phase responses of Purkinje cells support transient oscillations. eLife, 9: e60692.
Zang, Y*., De Schutter, E (2019). Climbing Fibers Provide Graded Error Signals in Cerebellar Learning. Frontiers in Systems Neuroscience, 13:46. *Correspondence author.
Zang, Y., Dieudonne, S. De Schutter, E (2018). Voltage‐ and branch‐specific climbing fiber responses in Purkinje cells. Cell Reports, 24: 1536-1549.
NeuroAI
Li, Y., Sun, X., Zang, Y. A Two-Stage Dendritic Network: Unsupervised Local Feature Learning with a Supervised Readout. Submitted. *Correspondence author.
Jing, R., Zhang, S., Li, X., Lv, L., Guo, D., Zang, Y. CERE-CRL: Utilization-Aware Plasticity Modulation for Sparse Continual Reinforcement Learning. Submitted. *Correspondence author.
Zhang, S., Jing, R., Li, X., Lv, L., Guo, D., Zang, Y. CeRA: Cerebellum-inspired Residual Adapter for Multi-Task Reinforcement Learning. Submitted. *Correspondence author.
Yang, Z., Bao, Y., Lv, L., Zhang, J., Li, X., Zang, Y∗. Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks. Submitted. *correspondence author. https://arxiv.org/pdf/2603.20687
Zhang, Y., Guo, D., Zang, Y∗. Cerebellum-Inspired Kernel for Robust OOD Detection. Submitted to IEEE TMI. *correspondence author. https://doi.org/10.64898/2026.03.11.710983
Zhang, S., Jing, R., Lv, L., Zhang, J., Zang, Y∗. A reinforcement learning framework inspired by cerebellar circuits and dendritic computational strategies. Submitted to IEEE TCDS. *correspondence author. https://arxiv.org/pdf/2602.15367
Wei X., Zhou, X., Yan, Y., Liu, X., Zang, Y∗., Yu, Q* (2026). Spike-based computations in recurrent spiking neural networks with bimodal neuronal time scales, Neural Networks, 200(108830). *co-correspondence author.
Zou, H#., Zang, Y#., Wu X., Ji, X* (2026). Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and reduced training time in pre-trained model-based continual representation learning. ICLR. #co-first author.
Li P., Ma Z., Zang Y., Yu Q (2026). Multi-synaptic cooperation: a bio-inspired framework for robust continual learning without network expansion. ICLR. *co-correspondence author.
Cai, W., Sun, H., He, J., Liao, Q., Zang, Y., Chen, D., Yao, D., Guo, D (2026). NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration. Neural Networks, 197(108417).
Zou, H#., Zang, Y#., Wu X., Zhu Y., Ji, X* (2025). FlyLoRA: boosting task decoupling and parameter efficiency via implicit rank-wise mixture-of-experts, NeurIPS. #co-first author.
Yue, X., Lv, L*., Liu, H., Zang, Y* (2025). LRR-UNet: a deep unfolding network with low-rank recovery for EEG signal denoising. CNS Neuroscience & Therapeutics, 31(10): e70632. *correspondence author.
Liu, J., Lv, L., Wang, P., Liu, H., Huang, Y., Zang, Y (2025). Tensor truncated schatten-p norm approximation tensor completion algorithm. IET Image Processing, 19: e70171.
Yang, Z., Si, X., Jin, W., Huang, D., Zang, Y., Yin, S., Ming. D (2025). SEEG Emotion recognition based on transformer network with channel Selection and Explainability. IEEE J Biomed Health Inform, 29(11):8153-8163.