The continual backpropagation algorithm can be enhanced by incorporating mechanisms for adaptive learning rates based on the importance of the units in the network. By dynamically adjusting the learning rates of individual units based on their relevance to the current task or data distribution, the algorithm can focus more on crucial information while maintaining plasticity. Additionally, introducing regularization techniques such as dropout or weight decay specifically tailored for continual learning scenarios can help prevent overfitting and promote generalization across tasks. Furthermore, exploring meta-learning approaches to adapt the learning process itself based on the network's performance over time can lead to more efficient and effective continual learning in deep neural networks.