论文标题
使用特定域的复发神经网络发现离子模型中隐藏状态
Discovery of the Hidden State in Ionic Models Using a Domain-Specific Recurrent Neural Network
论文作者
论文摘要
离子模型是描述可兴奋细胞状态的时间演化的一组普通微分方程（ODE），是神经和心脏电生理学建模的基石。现代离子模型可以具有数十个状态变量和数百个可调参数。将离子模型拟合到通常仅涵盖状态变量的有限子集的实验数据仍然是一个具有挑战性的问题。在本文中，我们描述了专门设计用于编码离子模型的经常性神经网络体系结构。该模型的核心是门控神经网络（GNN）层，捕获了经典（Hodgkin-Huxley）门控变量的动力学。该网络分为两个步骤：首先，它学习了用一组ODES编码的理论模型，其次，它是在实验数据上重新训练的。再培训网络是可解释的，因此可以将其结果纳入模型ODE。我们使用模拟的心室动作电位信号测试了GNN网络，并表明它可以推断出生理上可行的离子电流改变。在使用标准优化技术进行进一步微调之前，可以在数据同化的探索阶段中使用此类特定领域的神经网络。
Ionic models, the set of ordinary differential equations (ODEs) describing the time evolution of the state of excitable cells, are the cornerstone of modeling in neuro- and cardiac electrophysiology. Modern ionic models can have tens of state variables and hundreds of tunable parameters. Fitting ionic models to experimental data, which usually covers only a limited subset of state variables, remains a challenging problem. In this paper, we describe a recurrent neural network architecture designed specifically to encode ionic models. The core of the model is a Gating Neural Network (GNN) layer, capturing the dynamics of classic (Hodgkin-Huxley) gating variables. The network is trained in two steps: first, it learns the theoretical model coded in a set of ODEs, and second, it is retrained on experimental data. The retrained network is interpretable, such that its results can be incorporated back into the model ODEs. We tested the GNN networks using simulated ventricular action potential signals and showed that it could deduce physiologically-feasible alterations of ionic currents. Such domain-specific neural networks can be employed in the exploratory phase of data assimilation before further fine-tuning using standard optimization techniques.
