Beyond weight pruning, several other techniques can be employed to enhance the efficiency of event-based neural networks for language modeling. One approach is to incorporate quantization techniques to reduce the precision of weights and activations, leading to lower memory and computational requirements. Additionally, techniques like knowledge distillation can be utilized to transfer knowledge from a larger, more complex model to a smaller, more efficient event-based neural network. Furthermore, exploring sparsity-inducing regularizers, such as group lasso or elastic net, can encourage sparsity in both activations and weights, further improving efficiency without compromising performance. Techniques like dynamic network architectures, where the network structure evolves during training based on the task requirements, can also be explored to optimize efficiency in event-based neural networks for language modeling.