The inability to generalise to 'held out cells' warrants an investigation of the learning process itself. Fig. shows the number of training epochs for models used with different datasets, with and without dropout. It is reiterated that training was terminated by monitoring the validation loss, with a patience of 500 epochs. Therefore, if a model stopped training exactly on epoch 500 it would mean that its performance on the validation set never improved, even though its starting weights were set randomly. If training stopped soon after epoch 500, then the performance on the validation set either improved very little in the early epochs, possibly due to random fluctuations, or it improved to an optimum very rapidly. Fig. shows that models using a 'held out' cell as the validation dataset stopped training very early in most cases, indicating little or no learning. Examples of longer training are rarer, found mostly when dropout was applied, but still did not achieve a good test-set accuracy as discussed. Fig. shows that using a validation set containing waveforms from all cells (i.e. from the 'all cells' data split) extended the training process significantly, and in some instances training termination was due to reaching the maximum of 8000 epochs. The improvements that led to prolonged training in this case were likely due to the continuous learning and fine-tuning of cell-specific patterns. This process was counteracted when using dropout, resulting in shorter training.