用虚拟斑马鱼测试大模型发现神经机制，效果远超传统方法。
Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish
- 用大模型树搜索自动发现神经活动的机制模型。
- 加入感官输入后模型预测准确率显著提升。
- 结构先验对模型可解释性和泛化能力至关重要。
构建神经回路的机制模型是神经科学的根本目标，但模型验证受限于缺乏真实基准。为此，我们基于幼年斑马鱼的神经机械模拟构建了一个透明的仿真测试平台作为真实基准。结果表明，基于大语言模型的树搜索能自主发现预测性能显著优于现有预测基线的模型。仅依赖感官输入不足以实现真实的系统识别，因模型会利用统计捷径。结构先验对于实现鲁棒的分布外泛化及恢复可解释的机制模型至关重要。研究结果为真实神经记录建模提供指导，并为人工智能驱动的科学发现提供通用范式。
原文摘要 · Abstract (English)
Constructing mechanistic models of neural circuits is a fundamental goal of neuroscience, yet verifying such models is limited by the lack of ground truth. To rigorously test model discovery, we establish an in silico testbed using neuromechanical simulations of a larval zebrafish as a transparent ground truth. We find that LLM-based tree search autonomously discovers predictive models that significantly outperform established forecasting baselines. Conditioning on sensory drive is necessary but not sufficient for faithful system identification, as models exploit statistical shortcuts. Structural priors prove essential for enabling robust out-of-distribution generalization and recovery of interpretable mechanistic models. Our insights provide guidance for modeling real-world neural recordings and offer a broader template for AI-driven scientific discovery.
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