I lead a Computational Neuroscience Lab at Shenzhen University of Advanced Technology. Our research focuses on Data Analysis, Modeling, and Intelligence, leveraging computational approaches to advance our understanding of neural systems.

We are a diverse, interdisciplinary team—with expertise in physics, mathematics, computer science, statistics, biomedical engineering, and neuroscience—working at the intersection of AI and neuroscience. While we don’t conduct experiments ourselves, we collaborate closely with experimental neuroscientists, developing cutting-edge computational tools to accelerate neuroscience research.

Our ultimate mission is to uncover the shared principles of artificial and biological intelligence. In our lab, we code, collaborate, and discover.

My research lies at the intersection of computational neuroscience and machine learning, focusing on the applications of quantitative approaches to the study of the brain. With the adoption of increasingly powerful neurotechnology, novel quantitative approaches for understanding these new datasets of massive size and complexity are in increasing demand. My research interests are developing computational tools for processing and interpreting neuroscience data, and optimizing experimental designs and data acquisition through close collaborations with experimental neuroscientists. Data is the core of all my research and I am mostly interested in two central topics: 1, how to maximally identify the signals from the recorded raw data using the currently available experimental setups; 2, how to efficiently draw scientific conclusions from the data.

Includes study and work experience.

2020-Present,Assistant professor, ShenzhenUniversityof Advanced Technology

2017-2020, Postdoc, Columbia University, United States

2011-2016, Ph.D. in Neural Computation and Machine Learning, Carnegie Mellon University, United States

2006-2010, B.Sc. in Physics, University of Science and Technology of China

Selected publications

1.Yang, C.-Y., Shen, Y., Qi, X., Ding, L., Xiao, Y., Zhu, Q., Wang, H., Xu, C., Lau, P.-M., Zhou, P.#, Xu, F.#, Bi, G.-Q.#, 2024. IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution. Neurosci. Bull.https://doi.org/10.1007/s12264-024-01316-w(共通讯)

2.Xue, F., Li, F., Zhang, K., Ding, L., Wang, Y., Zhao, X., Xu, F., Zhang, D., Sun, M., Lau, P.-M., Zhu, Q., Zhou, P.#, Bi, G.-Q.#, 2023. Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes. National Science Review 11, nwad294.https://doi.org/10.1093/nsr/nwad294（共通讯）

3.Turner, N.L., Macrina, T., Bae, J.A., Yang, R., Wilson, A.M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A.L., Bleckert, A.A., Brittain, D., Froudarakis, E., Dorkenwald, S., Collman, F., Kemnitz, N., Ih, D., Silversmith, W.M., Zung, J., Zlateski, A., Tartavull, I., Yu, S., Popovych, S., Mu, S., Wong, W., Jordan, C.S., Castro, M., Buchanan, J., Bumbarger, D.J., Takeno, M., Torres, R., Mahalingam, G., Elabbady, L., Li, Y., Cobos, E.,Zhou, P., Suckow, S., Becker, L., Paninski, L., Polleux, F., Reimer, J., Tolias, A.S., Reid, R.C., da Costa, N.M., Seung, H.S., 2022. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 185, 1082-1100.e24.https://doi.org/10.1016/j.cell.2022.01.023

4.Lu, R., Liang, Y., Meng, G.,Zhou, P.,Svoboda, K., Paninski, L., Ji, N., 2020. Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nat Methods 17, 291–294.https://doi.org/10.1038/s41592-020-0760-9

5.Zhou, P., Resendez, S.L., Rodriguez-Romaguera, J., Jimenez, J.C., Neufeld, S.Q., Giovannucci, A., Friedrich, J., Pnevmatikakis, E.A., Stuber, G.D., Hen, R., Kheirbek, M.A., Sabatini, B.L., Kass, R.E., Paninski, L., 2018. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 7, 1–14.https://doi.org/10.7554/eLife.28728

6.Friedrich, J.,Zhou, P.,Paninski, L., 2017. Fast online deconvolution of calcium imaging data. PLOS Computational Biology 13, e1005423.https://doi.org/10.1371/journal.pcbi.1005423

7.Zhou, P., Burton, S.D., Urban, N.N., Ermentrout, G.B., 2013. Impact of neuronal heterogeneity on correlated colored noise-induced synchronization. Frontiers in computational neuroscience 7, 113–113.https://doi.org/10.3389/fncom.2013.00113

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