The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In this paper, we review applications of large-scale AI models across five major neuroscience domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, clinical decision support and translational frameworks, and disease-specific applications across neurological and psychiatric disorders. These models show potential to address major computational neuroscience challenges, including multimodal neural data integration, spatiotemporal pattern interpretation, and the development of translational frameworks for clinical research. Moreover, the interaction between neuroscience and AI has become increasingly reciprocal, as biologically informed architectural constraints are now incorporated to develop more interpretable and computationally efficient models. This review highlights both the promise of such technologies and critical implementation considerations, with particular emphasis on rigorous evaluation frameworks, effective integration of domain knowledge, prospective clinical validation, and comprehensive ethical guidelines. Finally, a systematic listing of critical neuroscience datasets used to develop and evaluate large-scale AI models across diverse research applications is provided.

翻译：大规模人工智能模型的发展正通过实现从原始脑信号和神经数据的端到端学习，深刻影响着神经科学研究。本文系统回顾了大规模人工智能模型在五大神经科学领域的应用：神经影像与数据处理、脑机接口与神经解码、临床决策支持与转化框架，以及跨神经与精神疾病的特定疾病应用。这些模型展现出解决计算神经科学关键挑战的潜力，包括多模态神经数据整合、时空模式解析，以及面向临床研究的转化框架开发。此外，神经科学与人工智能的互动日益呈现双向性，基于生物学启发的架构约束已被纳入模型开发，以提升模型的可解释性与计算效率。本综述既揭示了此类技术的广阔前景，也着重探讨了关键的实施考量，特别强调严谨的评估框架、领域知识的有效融合、前瞻性临床验证以及全面的伦理准则。最后，本文系统梳理了在不同研究应用中用于开发和评估大规模人工智能模型的重要神经科学数据集。