# Artificial intelligence as a surrogate brain: bridging neural dynamical models and data

## TL;DR

This paper explores how artificial intelligence can simulate brain dynamics, offering a new way to study and predict complex neural systems.

## Contribution

The paper introduces a unified framework for AI-based surrogate brains that integrates modeling, prediction, and evaluation of brain dynamics.

## Key findings

- AI-based surrogate brains can accurately predict future whole-brain dynamics using historical data.
- The framework enables simulation, analysis, and control of brain dynamics with high adaptability and non-linearity.
- Surrogate brains serve as a bridge between theoretical neuroscience and translational neuroengineering.

## 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, non-linearity 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.

In this review, mathematically principled AI surrogate brains unite theory and data to simulate, forecast, and optimally control brain dynamics, bridging neuroscience and engineering toward clinical translation.