Relying solely on neural networks for real-time incremental updates poses several drawbacks and limitations. Firstly, neural networks require significant computational resources which may not always be feasible for resource-constrained systems like embedded devices or IoT applications. Additionally, neural networks can suffer from catastrophic forgetting when new data is introduced without retaining past information adequately. This limitation can lead to suboptimal performance as the network adapts to new data at the expense of previously learned patterns. Moreover, training neural networks in real-time scenarios requires careful optimization to balance model complexity with inference speed.