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

Shihao Yang, Xiying Huang, Danilo Bernardo, Jun-En Ding, Andrew Michael, Jingmei Yang, Patrick Kwan, Ashish Raj, Feng Liu

TL;DR

This review analyzes how foundation and large-scale AI models are reshaping neuroscience by enabling end-to-end learning from diverse brain signals, across imaging, decoding, genomics, and clinical translation. It highlights transformer-based architectures, self-supervised and multimodal training, and data-grounded evaluation as central methods, while detailing domain-specific challenges and ethical considerations. Key contributions include a taxonomy of applications, a survey of representative models and datasets, and a roadmap for generalization, interpretability, and responsible deployment. The practical impact lies in improved neural decoding, cross-modal integration, and AI-augmented clinical decision support that can advance diagnostics, treatment, and brain-care research, provided robust governance and collaboration frameworks are established.

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.

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

TL;DR

This review analyzes how foundation and large-scale AI models are reshaping neuroscience by enabling end-to-end learning from diverse brain signals, across imaging, decoding, genomics, and clinical translation. It highlights transformer-based architectures, self-supervised and multimodal training, and data-grounded evaluation as central methods, while detailing domain-specific challenges and ethical considerations. Key contributions include a taxonomy of applications, a survey of representative models and datasets, and a roadmap for generalization, interpretability, and responsible deployment. The practical impact lies in improved neural decoding, cross-modal integration, and AI-augmented clinical decision support that can advance diagnostics, treatment, and brain-care research, provided robust governance and collaboration frameworks are established.

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.
Paper Structure (39 sections, 3 figures)

This paper contains 39 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background and Problem Formulation
  3. Theoretical Foundations of Large-scale AI Models
  4. From Classical Machine Learning to FMs
  5. Scaling Laws and Core Principles
  6. Architectural Frameworks and Design Principles
  7. Transformer Architecture in Neuroscience Applications
  8. Multimodal Integration Architectures
  9. Training Methodologies and Learning Paradigms
  10. Training Pipeline
  11. Challenges and Considerations for Neuroscience Applications
  12. Domain-Specific Challenges
  13. Tokenization and Neural Signal Representation
  14. Interpretability and Biological Plausibility
  15. Data Quality and Standardization
  16. ...and 24 more sections

Figures (3)

  • Figure 1: Neuroscience AI Landscape: Developmental Trajectory and Functional Framework.
  • Figure 2: Basic pipeline of Large-Scale AI Models
  • Figure 3: Overview of large-scale AI model applications in neuroscience. Five major application domains are shown, demonstrating the bidirectional relationship between neuroscience and AI development and identifying key research challenges.