Discovery of the Hidden State in Ionic Models Using a Domain-Specific Recurrent Neural Network
This work addresses a critical bottleneck in neuro- and cardiac electrophysiology modeling by providing a tool for exploratory data assimilation before fine-tuning, though it is incremental as it builds on existing neural network and optimization techniques.
The paper tackles the challenge of fitting complex ionic models with many state variables and parameters to limited experimental data by introducing a domain-specific recurrent neural network with a Gating Neural Network layer. The method successfully deduces physiologically-feasible alterations of ionic currents from simulated ventricular action potential signals, enabling interpretable results that can be incorporated back into model ODEs.
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.