{"ID":2857183,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10308","arxiv_id":"2510.10308","title":"Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data","abstract":"Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem, with the primary objective of accurately predicting future whole-brain dynamics with historical data. Here, we introduce a unified framework of constructing an AI-based surrogate brain that integrates forward modeling, inverse problem solving, and model evaluation. Leveraging the expressive power of AI models and large-scale brain data, surrogate brains open a new window for decoding neural systems and forecasting complex dynamics with high dimensionality, nonlinearity, and adaptability. We highlight that the learned surrogate brain serves as a simulation platform for dynamical systems analysis, virtual perturbation, and model-guided neurostimulation. We envision that the AI-based surrogate brain will provide a functional bridge between theoretical neuroscience and translational neuroengineering.","short_abstract":"Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven...","url_abs":"https://arxiv.org/abs/2510.10308","url_pdf":"https://arxiv.org/pdf/2510.10308v1","authors":"[\"Yinuo Zhang\",\"Demao Liu\",\"Zhichao Liang\",\"Jiani Cheng\",\"Kexin Lou\",\"Jinqiao Duan\",\"Ting Gao\",\"Bin Hu\",\"Quanying Liu\"]","published":"2025-10-11T18:23:10Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.NE\"]","methods":"[]","has_code":false}