Task specific hyperparameter tuning in reservoir computing is an open issue, and is of particular relevance for hardware implemented reservoirs. We investigate the influence of directly including externally controllable task specific timescales on the performance and hyperparameter sensitivity of reservoir computing approaches. We show that the need for hyperparameter optimisation can be reduced if timescales of the reservoir are tailored to the specific task. Our results are mainly relevant for temporal tasks requiring memory of past inputs, for example chaotic timeseries prediciton. We consider various methods of including task specific timescales in the reservoir computing approach and demonstrate the universality of our message by looking at both time-multiplexed and spatially multiplexed reservoir computing.

翻译：储层计算中的任务特定超参数调优是一个开放性问题，且与硬件实现的储层特别相关。我们研究了直接包含外部可控的任务特定时间尺度对储层计算方法性能及超参数敏感性的影响。结果表明，若根据具体任务定制储层的时间尺度，可减少对超参数优化的需求。我们的研究主要适用于需要记忆过去输入的时间任务（如混沌时间序列预测）。我们考察了多种在储层计算方法中纳入任务特定时间尺度的方式，并通过分析时间复用和空间复用这两种储层计算方案，验证了本结论的普遍适用性。