Predicting Neuronal Resting Potential Using LSTM Networks

I developed a computational model to simulate the resting potential of neurons, aiming to predict membrane voltage dynamics based on physiological parameters. I implemented and evaluated various neural network architectures, systematically exploring the impact of hidden dimensions, number of layers, learning rates, and regularization strategies. The results demonstrate that the models can accurately predict neuronal resting potentials, with strong agreement between predicted and observed values, providing a robust framework for understanding and modeling neuronal behavior.