发表机构
Beijing Normal University（北京师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过结构化潜变量模型测试HH模型，发现状态恢复不保证机制正确，需分别评估状态与动力学恢复。
AI 中文摘要
预测观测到的动力学并不能确立对潜在物理机制的恢复。机器学习能否重溯霍奇金-赫胥黎（HH）模型背后的隐状态推理？我们在模拟的电流和电压上训练结构化潜变量模型，在训练和模型选择中隐藏门控身份和轨迹。随后我们测试响应预测、状态恢复、协议迁移以及与HH动力学的一致性。在测试协议下，预测误差及其跨种子离散度在三个潜变量维度处急剧下降，而新协议下的门控恢复在五到六个坐标上有所改善。状态恢复取决于图表所使用的观测数据。观测到的电压相对于自由预测的电压，改善了电流钳解码。在电压钳下，向指令电压添加潜状态将m状态$R^2$从0.976提升至0.99以上，然而在相同平滑样本上，传输场与HH不一致。已知的可逆HH坐标在相同审计程序下实现了高快速m场一致性。一个精确的HH恒等式将差异分解为时间尺度加权的状态误差和传输场中的残差；这些项可能相互抵消或增强。这些发现涉及所测试的模型和图表。它们支持分别评估状态和动力学恢复，包括图表输入和跨干预的传输场一致性。
英文摘要
Predicting observed dynamics does not establish recovery of the underlying physical mechanism. Can machine learning retrace the hidden-state reasoning behind the Hodgkin-Huxley (HH) model? We train structured latent models on simulated current and voltage, withholding gate identities and trajectories from training and model selection. We then test response prediction, state recovery, protocol transfer, and agreement with HH dynamics. Prediction error and its cross-seed spread both drop sharply at three latent dimensions under the tested protocols, while gate recovery under new protocols improves through five to six coordinates. State recovery depends on which observations the chart uses. Observed voltage improves current-clamp decoding relative to freely predicted voltage. Under voltage clamp, adding latent state to command voltage raises m-state $R^2$ from 0.976 to above 0.99, yet the transported field disagrees with HH on identical smooth samples. Known invertible HH coordinates achieve high fast-m field agreement under the same audit procedure. An exact HH identity decomposes the discrepancy into time-scale-weighted state error and a residual in the transported field; these terms can cancel or reinforce. These findings concern the tested models and charts. They support evaluating state and dynamics recovery separately, including chart inputs and transported-field agreement across interventions.
Comments22 pages, 7 figures. Code available at https://github.com/factnn/hh-self-discovery
