In addition to random reinitialization of units, techniques such as elastic weight consolidation (EWC) and synaptic intelligence (SI) can be employed to maintain plasticity and adaptability in deep neural networks over time. EWC involves constraining the learning process to protect important parameters learned during previous tasks, thereby preventing catastrophic forgetting. On the other hand, SI estimates the importance of parameters based on their impact on the network's performance and selectively protects them during subsequent learning tasks. Moreover, techniques like progressive neural networks, which expand the network capacity as new tasks are encountered, and dynamic architecture approaches that adjust the network structure based on task requirements, can also contribute to preserving plasticity and adaptability in deep neural networks over time.