Reservoir computing (RC) is a powerful framework for learning and generating time-series data, including musical rhythmic patterns. However, its ability to capture and reproduce long-period rhythmic patterns remains limited. In this study, we address this limitation by leveraging slow feature analysis (SFA) to extract a slowly varying oscillatory feature that captures the characteristics of long-period patterns, specifically the periods of the target rhythms. We propose a novel approach that integrates the slow feature oscillation as an input to the RC model, thereby enhancing the learning and generation of long-period rhythmic patterns. The effectiveness of the proposed method is demonstrated through tasks involving synthetic time-series data containing simple long-period rhythmic patterns. The proposed method outperforms conventional RC in terms of rhythm reproduction and its fidelity. Furthermore, an experiment involving a human hi-hat drumming performance shows that the proposed method significantly improves the long-period rhythmic characteristics in the generated time-series data. These findings highlight the potential of combining SFA with RC to advance the modeling and generation of complex temporal patterns.

Reservoir computing, a machine learning framework used for modeling the brain, can predict temporal data with little observations and minimal computational resources. However, it is difficult to accurately reproduce the long-term target time series because the reservoir system becomes unstable. This predictive capability is required for a wide variety of time-series processing, including predictions of motor timing and chaotic dynamical systems. This study proposes oscillation-driven reservoir computing (ODRC) with feedback, where oscillatory signals are fed into a reservoir network to stabilize the network activity and induce complex reservoir dynamics. The ODRC can reproduce long-term target time series more accurately than conventional reservoir computing methods in a motor timing and chaotic time-series prediction tasks. Furthermore, it generates a time series similar to the target in the unexperienced period, that is, it can learn the abstract generative rules from limited observations. Given these significant improvements made by the simple and computationally inexpensive implementation, the ODRC would serve as a practical model of various time series data. Moreover, we will discuss biological implications of the ODRC, considering it as a model of neural oscillations and their cerebellar processors.

A critical aspect in robot technology lies in ensuring that the robot arm executes the intended task or movement. The conventional teaching–playback technique for programming robot arm movements generates excessive torque when the initial posture differs from that during motion teaching or when external disturbances disrupt robotic arm execution. To address this problem, this study proposed a motion generation system with echo state networks (ESNs) for robot arms. These networks can reproduce trajectories including features such as attractors and bifurcations by training time-series data originating from a dynamical system. By leveraging this capability, the proposed approach enables an ESN to infer a dynamical system underlying demonstrated motions to perform a specific task on the basis of state feedback. Subsequently, the trained ESN generates reference signals to replicate the learned motion based on the current robot state. These reference signals can be used for linear feedback at the joint level. Redundant robots can easily transition to unobserved states because of their degrees of freedom. However, the trained ESN exhibits remarkable generalization capabilities, effectively guiding the robot back to its intended motion in those unobserved states. Numerical simulations confirmed that the proposed system dynamically adapts to robot behavior, effectively mitigating excessive acceleration, even in scenarios with initial posture deviations or external disturbances, thereby outperforming conventional teaching–playback methods.

Adaptive kinematic/dynamic control of a robot arm (manipulator) is important for the robust execution of various tasks, including target tracking, when changes in the environment and robot’s physical parameters, such as mass and friction, occur. For highly accurate model-free adaptive tracking control, we propose an error-correction model using the reservoir of basal dynamics (reBASICS) computing method. reBASICS is trained to minimize multimodal errors, such as those in the end-effector position (visually detected) and torques (proprioceptively detected), and to correct robot trajectories. If errors are corrected simultaneously, the corrections can interfere with each other. Therefore, the model initially corrects torques to stabilize robot movements, followed by position correction. In the simulation of a two-link robot arm, approximate inverse kinematics (IK) and proportional-derivative (PD) controllers were assumed to produce tracking errors. The results showed that reducing errors based on reBASICS produced smaller tracking errors from the reference trajectory compared to that using a conventional echo state network (ESN). Furthermore, reducing both position and torque errors resulted in better performance than reducing position-only and torque-only errors.

Generation of complex patterns at a specific timing is crucial to most forms of learning and behavior, which are acquired through dopamine-modulated plasticity in the striatum. However, the neural mechanisms of such reward-based spatiotemporal processing remain unknown. Inspired by the cortico-striatal circuits, this study developed a new reservoir computing method, a class of recurrent neural networks, based on reward-modulated Hebbian learning (RMHL) for the spatiotemporal motor learning. We utilized a reservoir of basal dynamics (reBASICS), which generated self-sustained limit cycle oscillations with various frequencies, as a reservoir structure. Then, the oscillations were linearly integrated as readout output, in which readout weights were modulated with RMHL. The simulations showed that reBASICS-based RMHL was able to accomplish both motor timing and pattern drawing tasks, for which existing reservoir-based RMHL failed. Further, introducing an eligibility trace mechanism into RMHL allowed the model to learn motor timing even when reward-based modulation was delayed. In conclusion, this model is proposed as a new computational model of temporal processing of the striatum, where the cortical areas generate stable oscillations. From the oscillatory dynamics, spatiotemporal patterns are learned using RMHL in the striatum.

The ability of the brain to generate complex spatiotemporal patterns with specific timings is essential for motor learning and temporal processing. An approach that can model this function, using the spontaneous activity of a random neural network (RNN), is associated with orbital instability. We propose a simple system that learns an arbitrary time series as the linear sum of stable trajectories produced by several small network modules. New finding in computer experiments is that the trajectories of the module outputs are orthogonal to each other. They created a dynamic orthogonal basis acquiring a high representational capacity, which enabled the system to learn the timing of extremely long intervals, such as tens of seconds for a millisecond computation unit, and also the complex time series of Lorenz attractors. This self-sustained system satisfies the stability and orthogonality requirements and thus provides a new neurocomputing framework and perspective for the neural mechanisms of motor learning.

It is unclear how a human attributes the cause of failure to the robot in a human-robot interaction. We aim to identify the relationship between causal attribution and mind perception in a repeated game with an agent. We investigated causal attribution of the participant to the agent: which decision of the participant or the partner agent caused the unexpectedly small amount of the reward. We conducted experiments with three agent conditions: a human, robot, and computer. The results showed that the agency score negatively correlated with the degree of causal attribution to the partner agent. In particular, correlations of scores of “thought,” “memory,” “planning,” and “self-control” that are sub-items of agency were significant. This implied the impression that “the agent acted to succeed” might reduce causal attribution. In addition, we found that decrease in the scores of mind perception correlated with the degree of causal attribution to the partner agent. This suggests that a sense of betrayal of the prior expectation by the partner agent through the game might lead to causal attribution to the partner agent.

It has been suggested that perceptual immaturity in early infancy enhances learning for various cognitive functions. This paper demonstrates the role of visual development triggered by self-organization in a learner's visual space in a case of the mirror neuron system (MNS). A robot learns a function of the MNS by associating self-induced motor commands with observed motions while the observed motions are gradually self-organized in the visual space. A temporal convergence of the self-organization triggers visual development, which improves spatiotemporal blur filters for the robot's vision and thus further advances self-organization in the visual space. Experimental results show that the self-triggered development enables the robot to adaptively change the speed of the development (i.e., slower in the early stage and faster in the later stage) and thus to acquire clearer correspondence between self and other (i.e., the MNS).