We explore the effect of connectivity and topology on the dynamical behaviour of Reservoir Computers. At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently developed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the behavioural range of networks. It demonstrates how high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using dynamical behaviour to assess the quality of computing substrates, rather than evaluation through benchmark tasks that often provide a narrow and biased insight into the computing quality of physical systems.

我们探索了连通性和拓扑对储层计算机动态行为的影响。目前，在设计和手工制作物理储层计算机方面已付出了相当大的努力。结构和物理复杂性通常都是任务执行的关键，但是，评估它们的总体重要性是具有挑战性的。使用最新开发的框架，我们评估并比较了神经网络结构的动态自由度（指质量），以此作为物理系统的类比。结果量化了结构如何影响网络的行为范围。它说明了在网络规模更大的简单结构中，通常也可以通过更复杂的结构实现高质量的效果。另外，在较小的网络中，通常通过增加连接的复杂性来提高质量。