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：本文表明，神经元和突触动力学的异质性能够降低循环脉冲神经网络（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。我们从理论上证明，神经元整合/松弛动力学的多样性增强了RSNN学习更多不同输入模式的能力（更高的记忆容量），从而提升分类和预测性能。进一步证明，异质性尖峰时间依赖可塑性（STDP）突触动力学能够降低脉冲活动，同时保持记忆容量。理论分析推动采用贝叶斯优化设计异质性RSNN，以确定神经元和突触的异质性，从而优化$\mathcal{E}$（定义为脉冲活动与记忆容量之比）。在时间序列分类与预测任务上的实证结果表明，与同质性RSNN相比，优化后的异质性RSNN（HRSNN）在提升性能的同时降低了脉冲活动。