Beyond training duration and task similarity, several other factors can influence the interplay between neural collapse and plasticity loss in deep learning models. One significant factor is the optimization algorithm used during training. Different optimization techniques, such as stochastic gradient descent (SGD), Adam, or RMSprop, can impact how quickly a model converges and how susceptible it is to neural collapse or plasticity loss. The choice of regularization methods, such as L1 or L2 regularization, can also affect the model's ability to adapt and retain information over time. Moreover, the initialization strategy for the network weights, the learning rate schedule, and the presence of batch normalization layers can all contribute to the dynamics of neural collapse and plasticity loss. Additionally, the presence of noisy or incomplete data, the quality of the training data, and the presence of adversarial examples can further complicate the relationship between these phenomena.