In addition to the initial scale κ, several other factors can significantly influence the training dynamics of neural networks. One crucial factor is the choice of activation functions, which can impact the network's capacity to learn complex patterns and representations. Different activation functions introduce non-linearities that affect the network's ability to model intricate relationships in the data. Additionally, the network architecture, including the number of layers, the width of the layers, and the connectivity patterns, plays a vital role in shaping the learning dynamics. Regularization techniques, such as dropout or weight decay, can also influence the training behavior by controlling model complexity and mitigating overfitting. By incorporating these factors into the analysis alongside the initial scale κ, researchers can gain a more comprehensive understanding of the training dynamics and optimize neural network performance.