In recent years, swimming robots have been developed to achieve efficient propulsion and high maneuverability that are possessed naturally by fish. Previous studies have attempted to achieve swimming similar to fish by control based on physical models and top-down architectures, but have encountered problems due to the high complexity of the underwater environment. Several research works have tried to overcome these problems by exploiting embodiment—that is, by mimicking the physical properties of fish. To achieve more intelligent swimming from the perspective of the embodiment, we focused on a framework called physical reservoir computing (PRC). This framework allows us to utilize physical dynamics as a computational resource. In this study, we propose a soft sheet-like swimming robot and a PRC-based architecture that can be used to emulate swimming motions by exploiting its own body dynamics for closed-loop control. Through experiments, we demonstrated that our system satisfies the properties required for learning swimming motion through supervised learning. We also succeeded in robust motion generation and environmental state estimation, opening up future prospects for more intelligent robot control and sensing.

How does a single spiking neuron process information? This question is a long lasting one, which has been constantly posed and pursued by many researchers from different perspectives. In this paper, we tackle this issue from the perspective of reservoir computing using a single Izhikevich neuron as a model system. To prepare reservoir nodes from the response of a single Izhikevich neuron, we used of a technique called time multiplexing, which exploits a time-scale difference between input-output series and the transient dynamics of the single neuron. Based on this scheme, we evaluated the information processing capability of a single Izhikevich neuron using a standard benchmark task. Furthermore, we measured its memory capacity and showed its characteristic memory profile in various parameter settings. Finally, the relationships between the dynamical properties of the Izhikevich neuron and its memory capacity are discussed in detail.