Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves together three groundbreaking studies that revolutionize SNN performance through the introduction of heterogeneity in neuron and synapse dynamics. We explore the transformative impact of Heterogeneous Recurrent Spiking Neural Networks (HRSNNs), supported by rigorous analytical frameworks and novel pruning methods like Lyapunov Noise Pruning (LNP). Our findings reveal how heterogeneity not only enhances classification performance but also reduces spiking activity, leading to more efficient and robust networks. By bridging theoretical insights with practical applications, this comprehensive summary highlights the potential of SNNs to outperform traditional neural networks while maintaining lower computational costs. Join us on a journey through the cutting-edge advancements that pave the way for the future of intelligent, energy-efficient neural computing.

通过引入神经元和突触动态的异质性，本文结合了三项开创性的研究，从而革新了脉冲神经网络的性能。我们探索了异质性循环脉冲神经网络（HRSNNs）的转变性影响，支持严格的分析框架和新颖的修剪方法，如Lyapunov Noise Pruning（LNP）。我们的发现揭示了异质性不仅可以提高分类性能，而且还可以减少脉冲活动，从而实现更高效和更强大的网络。通过将理论洞见与实际应用结合起来，这个全面的摘要突显了脉冲神经网络在维持较低计算成本的同时超越传统神经网络的潜力。让我们一起领略为智能、高效的神经计算铺平道路的最前沿进展。

利用时间尺度的异质性进行稀疏循环脉冲神经网络，以实现高效能边缘计算