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 four 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通过支持从原始脑信号和神经数据中进行端到端学习，正在深刻影响神经科学研究。本文系统回顾了大规模人工智能模型在四大神经科学核心领域中的应用：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，以及神经系统及精神疾病的特定应用。这些模型展现出了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、时空模式解读，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之间的互动日益呈现双向性——生物启发的结构约束现被引入模型设计，以开发更具可解释性和计算效率的模型。本综述既阐述了这类技术的广阔前景，也强调了关键的实施考量因素，特别是严格的评估框架、领域知识的有效整合、前瞻性临床验证以及全面的伦理准则。最后，本文系统列出了用于开发和评估多样化研究应用中大规模人工智能模型的关键神经科学数据集。