The advent of large-scale artificial intelligence (AI) models has a transformative effect on neuroscience research, which represents a paradigm shift from the traditional computational methods through the facilitation of end-to-end learning from raw brain signals and neural data. In this paper, we explore the transformative effects of large-scale AI models on five major neuroscience domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, molecular neuroscience and genomic modeling, clinical assistance and translational frameworks, and disease-specific applications across neurological and psychiatric disorders. These models are demonstrated to address major computational neuroscience challenges, including multimodal neural data integration, spatiotemporal pattern interpretation, and the derivation of translational frameworks for clinical deployment. 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 notable promise of such technologies and key implementation considerations, with particular emphasis on rigorous evaluation frameworks, effective domain knowledge integration, and comprehensive ethical guidelines for clinical use. Finally, a systematic listing of critical neuroscience datasets used to derive and validate large-scale AI models across diverse research applications is provided.

翻译：大规模人工智能（AI）模型的出现对神经科学研究产生了变革性影响，这代表了一种从传统计算方法向通过促进从原始脑信号和神经数据进行端到端学习的范式转变。本文探讨了大规模AI模型对五个主要神经科学领域的变革性影响：神经影像与数据处理、脑机接口与神经解码、分子神经科学与基因组建模、临床辅助与转化框架，以及神经和精神疾病中的特定疾病应用。这些模型被证明能够应对计算神经科学的主要挑战，包括多模态神经数据整合、时空模式解释，以及为临床部署推导转化框架。此外，神经科学与AI之间的互动已变得越来越具有互惠性，因为现在融入了生物学启发的架构约束，以开发更具可解释性和计算效率的模型。本综述强调了此类技术的显著前景以及关键的实施考量，特别侧重于严格的评估框架、有效的领域知识整合以及临床应用的全面伦理准则。最后，本文系统地列出了用于在不同研究应用中推导和验证大规模AI模型的关键神经科学数据集。