Experiments using conventional experimental approaches to capture the dynamics of ion channels are not always feasible, and even when possible and feasible, some can be time-consuming. In this work, the ionic current–time dynamics during cardiac action potentials (APs) are predicted from a single AP waveform by means of artificial neural networks (ANNs). The data collection is accomplished by the use of a single-cell model to run electrophysiological simulations in order to identify ionic currents based on fluctuations in ion channel conductance. The relevant ionic currents, as well as the corresponding cardiac AP, are then calculated and fed into the ANN algorithm, which predicts the desired currents solely based on the AP curve. The validity of the proposed methodology for the Bayesian approach is demonstrated by the R (validation) scores obtained from training data, test data, and the entire data set. The Bayesian regularization’s (BR) strength and dependability are further supported by error values and the regression presentations, all of which are positive indicators. As a result of the high convergence between the simulated currents and the currents generated by including the efficacy of a developed Bayesian solver, it is possible to generate behavior of ionic currents during time for the desired AP waveform for any electrical excitable cell.

使用传统实验方法捕获离子通道动力学的实验并不总是可行的，即使在可能和可行的情况下，有些实验也可能很耗时。在这项工作中，心脏动作电位 (AP) 期间的离子电流-时间动力学是通过人工神经网络 (ANN) 从单个 AP 波形预测的。数据收集是通过使用单细胞模型运行电生理模拟来完成的，以便根据离子通道电导的波动识别离子电流。然后计算相关的离子电流以及相应的心脏 AP，并将其输入 ANN 算法，该算法仅根据 AP 曲线预测所需的电流。从训练数据、测试数据和整个数据集中获得的 R（验证）分数证明了所提议的贝叶斯方法方法的有效性。误差值和回归表示进一步支持贝叶斯正则化 (BR) 的强度和可靠性，所有这些都是积极的指标。由于模拟电流与通过包括开发的贝叶斯求解器的功效产生的电流之间的高度收敛，可以在任何电可激发细胞的所需 AP 波形的时间内产生离子电流行为。