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.

• →Gradient-based learning rules achieve the highest accuracy in spiking neural networks but require significant computational resources.
• →Bio-inspired learning rules like Tempotron and SpikeProp provide favorable accuracy-efficiency trade-offs for practical applications.
• →Lempel-Ziv complexity serves as an effective descriptor for quantifying temporal organization in spike-train patterns.
• →The choice of learning paradigm significantly affects both classification performance and computational cost in temporal pattern recognition.
• →Learning rule selection should be guided by application constraints and the desired balance between performance and computational overhead.