发现神经活动的机理模型：在虚拟斑马鱼中的系统识别
Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish
AI总结：
本文通过虚拟斑马鱼实验，利用大语言模型树搜索发现神经活动的预测模型，证明结构先验对机理模型发现的重要性。
摘要：
构建神经回路的机理模型是神经科学的基本目标，但验证此类模型受到缺乏真实数据的限制。为了严格测试模型发现，我们建立了一个虚拟测试环境，利用斑马鱼幼体的神经机械模拟作为透明的真实数据。我们发现基于大语言模型的树搜索能够自动发现预测模型，这些模型显著优于已建立的预测基线。在感官驱动条件下是必要的，但并不充分，因为模型利用了统计捷径。结构先验证明对于实现鲁棒的分布外泛化和恢复可解释的机理模型至关重要。我们的见解为建模真实世界神经记录提供了指导，并提供了一个更广泛的AI驱动科学发现的模板。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
