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.

翻译：构建神经回路的机制模型是神经科学的一个基本目标，但此类模型的验证因缺乏真实基准而受限。为严格检验模型发现方法，我们利用幼年斑马鱼的神经力学模拟建立了一个硅基测试平台，作为透明的真实基准。研究发现，基于大语言模型的树搜索能够自主发现预测模型，其性能显著优于既有的预测基线。尽管感知驱动条件是实现忠实系统辨识的必要条件，但并非充分条件，因为模型可能利用统计捷径。结构先验被证明对于实现稳健的分布外泛化及可解释机制模型的复原至关重要。我们的研究结果为真实世界神经记录数据的建模提供了指导，并为人工智能驱动的科学发现提供了更广泛的范式模板。