![[2510.16658] Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.16658] Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review
Summary
This comprehensive review explores the impact of large-scale AI models on neuroscience, detailing their applications in neuroimaging, brain-computer interfaces, and clinical decision support.
Why It Matters
The integration of AI in neuroscience is transforming research methodologies and clinical practices. Understanding these advancements is crucial for researchers and practitioners aiming to leverage AI for improved outcomes in neurological and psychiatric disorders.
Key Takeaways
- Large-scale AI models enhance the analysis of complex neural data.
- Applications span neuroimaging, brain-computer interfaces, and clinical frameworks.
- The review emphasizes the importance of ethical guidelines and rigorous evaluation in AI implementation in neuroscience.
Computer Science > Artificial Intelligence arXiv:2510.16658 (cs) [Submitted on 18 Oct 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review Authors:Shihao Yang, Xiying Huang, Danilo Bernardo, Jun-En Ding, Andrew Michael, Jingmei Yang, Patrick Kwan, Ashish Raj, Feng Liu View a PDF of the paper titled Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review, by Shihao Yang and 8 other authors View PDF HTML (experimental) Abstract: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 m...
