Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review

2025-10-18

AI Summary

Conventional computational approaches face fundamental bottlenecks in analyzing raw neural signals, integrating multimodal neurodata, interpreting spatiotemporal patterns, and enabling clinical translation. Method: This study systematically reviews transformative applications of large-scale AI models across five domains—neuroimaging, brain–computer interfaces, molecular neuroscience, clinical decision support, and disease modeling—and proposes a “neuroscience–AI bidirectional empowerment” framework. The framework integrates biologically inspired modeling, self-supervised learning, multimodal spatiotemporal fusion, and explainable AI (XAI), rigorously constrained by domain knowledge and ethical principles. Contribution/Results: We establish a standardized neuroscience dataset ecosystem and multidimensional evaluation benchmarks. These advances significantly enhance model interpretability, computational efficiency, and clinical adaptability, thereby accelerating end-to-end neural data analysis toward trustworthy, scalable, and clinically deployable solutions.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Integrating multimodal neural data across neuroscience domains
• Interpreting spatiotemporal patterns in brain activity data
• Developing clinically deployable translational AI frameworks

Innovation

• Large-scale AI models enable end-to-end learning from neural data
• Models integrate multimodal neural data and interpret spatiotemporal patterns
• Biologically informed constraints enhance model interpretability and efficiency