Biophysical neuron models link measurements of neural activity to underlying cellular mechanisms. Yet, a central challenge is that the kinetics of many ion channels are poorly characterized, and practical simplifications -- omitting channels or reducing morphological detail -- introduce systematic gaps between model and biology. Bridging these gaps requires approaches that can flexibly discover unmodeled dynamics while preserving mechanistic interpretability. Here, we introduce a hybrid modeling framework that embeds neural ordinary differential equations into conductance-based biophysical models to capture unknown currents or mis-specified channel kinetics. By parameterizing the neural ODE in terms of voltage-dependent steady-state and time-constant functions, we recover interpretable gating dynamics directly from voltage recordings without assuming a functional form. We show that the hybrid model fits the gating kinetics of 2400 ion channel models and recovers unknown gating dynamics from single current-clamp recordings, generalizing to out-of-distribution stimulus regimes under realistic inputs and parameter misspecification. We also use our method to reduce a multicompartment model of a cortical neuron into a single-compartment hybrid model with a learned axial current, yielding up to an order of magnitude lower computational cost. Together, our results establish a plug-and-play framework for selectively replacing unknown components of conductance-based models with neural ODEs while preserving their mechanistic structure.

翻译：生物物理神经元模型将神经活动测量与潜在细胞机制联系起来。然而，一个核心挑战在于许多离子通道的动力学特性尚未得到充分表征，而实际简化——例如忽略某些通道或减少形态细节——会在模型与生物学之间引入系统性差距。弥合这些差距需要能够灵活发现未建模动力学同时保持机械论可解释性的方法。在此，我们提出一种混合建模框架，将神经常微分方程嵌入基于电导的生物物理模型中，以捕捉未知电流或误指定的通道动力学。通过将神经ODE参数化为电压依赖的稳态和时间常数函数，我们无需假设函数形式即可直接从电压记录中恢复可解释的门控动力学。我们证明，该混合模型能拟合2400个离子通道模型的门控动力学，并可从单个电流钳记录中恢复未知门控动力学，在现实输入和参数误指定条件下泛化至分布外刺激模式。我们还利用该方法将皮层神经元的多房室模型简化为具有学习到的轴向电流的单房室混合模型，计算成本降低了一个数量级。综上，我们的结果建立了一个即插即用框架，可在保持机械论结构的同时，选择性替换基于电导模型中未知组件的神经ODE。