Reservoir computing (RC) is a brain-inspired computing framework that employs a transient dynamical system whose reaction to an input signal is transformed to a target output. One of the central problems in RC is to find a reliable reservoir with a large criticality, since computing performance of a reservoir is maximized near the phase transition. In this work, we propose a continuous reservoir that utilizes transient dynamics of coupled chaotic oscillators in a critical regime where sudden amplitude death occurs. This “explosive death” not only brings the system a large criticality which provides a variety of orbits for computing, but also stabilizes them which otherwise diverge soon in chaotic units. The proposed framework shows better results in tasks for signal reconstructions than RC based on explosive synchronization of regular phase oscillators. We also show that the information capacity of the reservoirs can be used as a predictive measure for computational capability of a reservoir at a critical point.

储层计算（RC）是一个灵感来自大脑的计算框架，它采用了瞬态动力系统，其对输入信号的反应被转换为目标输出。RC中的中心问题之一是找到一个具有大临界值的可靠储层，因为在相变附近，储层的计算性能会最大化。在这项工作中，我们提出了一个连续油藏，该油藏在出现突然幅度死亡的临界状态下利用耦合混沌振荡器的瞬态动力学。这种“爆炸性死亡”不仅给系统带来了巨大的危险，它为计算提供了多种轨道，而且还使它们稳定下来，否则它们很快就会在混沌单位中发散。所提出的框架在信号重建任务中比基于规则相位振荡器的爆炸性同步的RC表现出更好的结果。我们还表明，储层的信息容量可用作临界点处储层计算能力的预测指标。