用纳米神经形态网络构建动态储池，稀疏结构更利于时序预测。
Dynamic Reservoir Computing with Physical Neuromorphic Networks
- 利用非线性电路元件实现节点与边的耦合，形成动态储池。
- 稀疏网络比密集网络产生更优的非线性时序输出，提升预测能力。
- 适合需要建模复杂混沌系统的科研人员和硬件加速研究者。
物理储池计算（RC）依赖对特定物理系统结构与内部动态的理解。本研究探索具有神经形态特性的纳米电子网络作为物理储池在RC框架中的应用。这些网络通过非线性纳米电路元件使节点活动与边动态耦合，输出受网络连通性影响。研究发现，不同稀疏度的网络相比密集网络能生成更有效的非线性时序响应。在自主多变量混沌时间序列预测任务中，稀疏网络有助于维持网络活跃度与整体动态，从而成功学习洛伦兹63系统吸引子行为。
原文摘要 · Abstract (English)
Reservoir Computing (RC) with physical systems requires an understanding of the underlying structure and internal dynamics of the specific physical reservoir. In this study, physical nano-electronic networks with neuromorphic dynamics are investigated for their use as physical reservoirs in an RC framework. These neuromorphic networks operate as dynamic reservoirs, with node activities in general coupled to the edge dynamics through nonlinear nano-electronic circuit elements, and the reservoir outputs influenced by the underlying network connectivity structure. This study finds that networks with varying degrees of sparsity generate more useful nonlinear temporal outputs for dynamic RC compared to dense networks. Dynamic RC is also tested on an autonomous multivariate chaotic time series prediction task with networks of varying densities, which revealed the importance of network sparsity in maintaining network activity and overall dynamics, that in turn enabled the learning of the chaotic Lorenz63 system's attractor behavior.
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