The time-delay-based reservoir computing setup has seen tremendous success in both experiment and simulation. It allows for the construction of large neuromorphic computing systems with only few components. However, until now the interplay of the different timescales has not been investigated thoroughly. In this manuscript, we investigate the effects of a mismatch between the time-delay and the clock cycle for a general model. Typically, these two time scales are considered to be equal. Here we show that the case of equal or resonant time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir. In particular, we can show that non-resonant ratios of these time scales have maximal memory capacities. We achieve this by translating the periodically driven delay-dynamical system into an equivalent network. Networks that originate from a system with resonant delay-times and clock cycles fail to utilize all of their degrees of freedom, which causes the degradation of their performance.

中文翻译：

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通过非谐振时钟周期提高延时油藏计算的性能。

基于时间的储层计算设置在实验和模拟中均取得了巨大的成功。它允许仅使用少量组件即可构建大型神经形态计算系统。但是，到目前为止，尚未深入研究不同时间范围的相互作用。在此手稿中，我们研究了通用模型的时间延迟和时钟周期之间不匹配的影响。通常，这两个时标被认为是相等的。在这里，我们表明等时或共振时滞和时钟周期的情况可能会受到不利影响，并导致油藏逼近误差增加。特别是，我们可以证明这些时间尺度的非谐振比率具有最大的存储容量。我们通过将周期性驱动的时滞动力系统转换为等效网络来实现这一目标。源自具有共振延迟时间和时钟周期的系统的网络无法利用其所有自由度，这会导致其性能下降。