There are many methodologies in which neural networks can have their weights modified independently. These include asynchronous updates, non-gradient-based optimization algorithms, regularization techniques, perturbations, and evolutionary approaches. These methods can enhance the performance of neural networks by diversifying the strategies used to adjust weights, thus potentially leading to better generalization and robustness. PyTorch offers a