Accuracy-Efficiency Trade-Offs in Spiking Neural Networks: A Lempel-Ziv Complexity Perspective on Learning Rules

Researchers developed a framework using Lempel-Ziv complexity to evaluate trade-offs between accuracy and computational efficiency in spiking neural networks. The study found that gradient-based learning achieves highest accuracy but at high computational cost, while bio-inspired learning rules offer better efficiency trade-offs for temporal pattern recognition tasks.