This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve $\mathcal{E}$, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN.

翻译：本文揭示，神经元与突触动力学的异质性能够降低递归脉冲神经网络（RSNN）的脉冲活动，同时提升预测性能，从而实现脉冲高效（无监督）学习。我们通过理论分析表明，神经元整合/松弛动力学的多样性可增强RSNN学习更多独特输入模式的能力（即更高的记忆容量），从而改善分类与预测性能。进一步证明，突触的异质性脉冲时序依赖可塑性（STDP）动力学可降低脉冲活动，同时保持记忆容量。基于这些理论结果，我们提出采用贝叶斯优化方法设计异质性RSNN，通过确定神经元与突触的最优异质性参数来优化指标$\mathcal{E}$（定义为脉冲活动与记忆容量的比值）。时间序列分类与预测任务的实验结果表明，与同质性RSNN相比，优化的异质性RSNN在提升性能的同时显著降低了脉冲活动。