报告摘要：
Recent advances in AI and machine learning have dramatically changed the landscape of data-driven applications. I will first discuss some challenges in analyses of large-scale neural data ranging from electrophysiology, calcium imaging, to EEG and fMRI data. Next, I will show how generative AI and deep representational learning have provided new opportunities for neural data analysis. I will present several neuroscience applications including neural population decoding based on Neural Transformer and transfer learning and EEG data augmentation based on generative AI models. Finally, I will discuss some NeuroAI applications and emerging brain-to-content technologies, including Brain-Machine Interface (BMI) and development of neuro-inspired neural network models.
报告人简介（Short Bio）: Zhe Sage Chen is a tenured associate professor and principal investigator at New York University (NYU), with joint appointment at the Department of Psychiatry and Department of Neuroscience and Physiology at NYU Grossman School of Medicine, Department of Biomedical Engineering at NYU Tandon School of Engineering. He is the Founding Director of the CN^3 (Computational Neuroscience, Neuroengineering and Neuropsychiatry) Laboratory at NYU, and Program Director of the Computational Psychiatry program at NYU. The research in his lab covers a wide range of areas in computational neuroscience, neural engineering, machine learning, and brain-machine interfaces, studying fundamental research questions related to sleep and memory, nociception and pain, and cognitive control. His research and collaborative work has been published in high-impact journals such as Nature, Nature Biomedical Engineering, Science Translational Medicine, Cell, Neuron, Cell, Cell Reports, and Nature Communications. He has served in the editorial board and the role of associate editor for Neural Networks, Journal of Neural Engineering, Frontiers in Computational Neuroscience and IEEE Transactions on Neural Systems & Rehabilitation Engineering.
