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In artificial Spiking Neural Networks (SNNs) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. In biological systems there are numerous examples of autonomously generated periodic activities. Several different periodic patterns are generated simultaneously in a living body. It is known that in biological systems there are specific neurons which generate such periodic patterns. This paper presents a method for synthesis of neural oscillators by spiking neural networks. We propose a learning method for synthesizing spiking neural networks which generate desired periodic spike trains with specified spike emission times. We also propose a method for making the periodic trajectory generated by the synthesized spiking neural oscillator asymptotically stable., This is a product of research which was financially supported in part by the Kansai University Fund for the Promotion and Enhancement of Education and Research, 2018, “Development of System Analysis and Design Methods Integrating Model and Data Premised on IOT Technology ”., Date of Conference: 18-22 July 2021 Conference Location: Shenzhen, China

For the crawl stroke in swimming, it is important that the stroke made by the upper limbs and the flutter kick made by the lower limbs are well coordinated in order to enhance swimming performance. However, the training method to acquire the appropriate flutter kick timing has not been sufficiently established. In the present study, a biofeedback training system for swimmers to acquire appropriate kick timing was proposed. In this system, the most difficult and important part is the estimation of the appropriate kick timing. Therefore, the objective of this study was to develop a kick timing estimation algorithm in the crawl stroke for biofeedback training system using neural oscillators. First, a CPG network which outputs the kick estimation timing according to the input was constructed. In order to synchronize the roll angle in the CPG network with the actual one measured by a sensor, a special algorithm to change the cycle of the oscillation for the CPG network was introduced. Validation for the output of sinusoidal input accompanying sudden change in cycle was examined. It was found that the output signal for the roll tracked the input signal well, despite the sudden change in cycle. Validation for the actual input obtained in the experiment was next examined. It was found that the output from the CPG network was sufficiently consistent with the experimental values, suggesting sufficient performance of the proposed estimation algorithm.

The objective of this study was to propose a CPG network which can generate the swimming motion of the crawl stroke. First, the CPG network for legs performing a flutter kick was constructed by connecting the neural oscillators for the leg joints. The flutter kick motion was successfully generated by the proposed CPG network. The propulsion by the generated flutter kick motion was confirmed by the simulation of the swimming movement. Next, the CPG network for both the arms and legs were constructed, in which the neural oscillator for the arms initiated the trigger signal to start the prescribed stroke motion. By changing the intrinsic cycle of the neural oscillators for the legs, both six- and two-beat crawls could be realized. It was also found that a stable region with respect to the relationship between the intrinsic cycles of the neural oscillators for the arms and legs certainly existed for the six-beat crawl, although the intrinsic cycles of the arms were three times longer than those of the legs in this case. The propulsion by the generated swimming motion was confirmed by the simulation of the swimming movement both for the six- and two-beat crawls. Finally, the roll angle of the swimmer was fed back into the CPG network in order to restore the balance in the roll direction. Restoring the balance in the roll direction was successfully realized by the proposed feedback algorithm. The resultant motion showed a complicated behavior, such as skipping strokes.

The involvement of glial cells in the regulation of physiological functions is being increasingly recognized, yet their role in plasticity of neural oscillators has remained largely elusive. An excellent model system to address the latter function is the pacemaker nucleus of the weakly electric fish, Apteronotus leptorhynchus. This brainstem oscillator drives the fish's electric organ discharge in a one-to-one fashion, with median frequencies of 880 Hz in males and 740 Hz in females. Morphometric analysis of the pacemaker nucleus has shown that astrocytes outnumber mature neurons seven-fold, and oscillator neurons even 200-fold. A similar dominance of astrocytes occurs among the adult-born cells that differentiate into glia and neurons. The astrocytes form a dense meshwork of cells interconnected by gap junctions. The degree of association of astrocytic fibers with the neural oscillator cells, and the gap-junction coupling between individual astrocytes, exhibit a sexual dimorphism, which parallels the sexual dimorphisms in the output frequency of the pacemaker nucleus, and ultimately in the electric organ discharge of the fish. It is hypothesized that the dynamics in astroglial structure mediate differences in the capacity to buffer potassium, which increases during the generation of action potentials. These differences, in turn, affect the excitability of the neural oscillator cells, and thus the output frequency of the pacemaker nucleus. Comparison of the pacemaker nucleus with other brain oscillators suggests that modulation of the output activity is one of the chief functions of the interaction of glia with the neural oscillator cells.

On account of their role played in the fundamental biological rhythms and by considering their potential use in information processing, the dynamical properties of an artificial neural network are particularly interesting to investigate. In order to reduce the degree of complexity of this work, we have considered in this paper a fully connected neural network of two discrete neurons. We have proceeded to a qualitative and quantitative study of their state evolution by means of numerical simulation. The first aim was to find the possible equilibrium states. Other authors have already shown that some oscillating state can occur. So, the second aim was to analyze the dynamical properties of each of them. We have computed the value of the Lyapunov’s exponents and the fractal dimension. The sensitivity of the dynamical characteristics to parameters such as the weights of the connections and the shape of the activation function has been studied.

Taxis behaviour in Drosophila larva is thought to consist of distinct control mechanisms triggering specific actions. Here, we support a simpler hypothesis: that taxis results from direct sensory modulation of continuous lateral oscillations of the anterior body, sparing the need for ‘action selection’. Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. The mechanism provides a simple architecture for combining information across modalities, and explaining how learnt associations modulate taxis. We discuss the results in the light of larval neural circuitry and make testable predictions.

Taxis behaviour in Drosophila larva is thought to consist of distinct control mechanisms triggering specific actions. Here, we support a simpler hypothesis: that taxis results from direct sensory modulation of continuous lateral oscillations of the anterior body, sparing the need for ‘action selection’. Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. The mechanism provides a simple architecture for combining information across modalities, and explaining how learnt associations modulate taxis. We discuss the results in the light of larval neural circuitry and make testable predictions. DOI: http://dx.doi.org/10.7554/eLife.15504.001

Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

We address a neural-oscillator-based control scheme to achieve biologically inspired motion generation. In general, it is known that humans or animals exhibit novel adaptive behaviors regardless of their kinematic configurations against unexpected disturbances or environment changes. This is caused by the entrainment property of the neural oscillator which plays a key role to adapt their nervous system to the natural frequency of the interacted environments. Thus we focus on a self-adapting robot arm control to attain natural adaptive motions as a controller employing neural oscillators. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a single pendulum coupled with the neural oscillator in simulation and experiment. Then this work shows the performance of the robot arm coupled to neural oscillators through various tasks that the arm traces a trajectory. With these, the real-time closed-loop system allowing sensory feedback of the neural oscillator for the entrainment property is proposed. In particular, we verify an impressive capability of biologically inspired self-adaptation behaviors that enables the robot arm to make adaptive motions corresponding to an unexpected environmental variety.

We propose an efficient and powerful alternative for adaptation of human motions to humanoid robots keeping the bipedal stability. For achieving a stable and robust whole body motion of humanoid robots, we design a biologically inspired control framework based on neural oscillators.Entrainments of neural oscillators play a key role to adapt the nervous system to the natural frequency of the interacted environments, which show superior features when coupled with virtual components. The coupled system allows an unstable system to stably move according to environmental changes. Hence the feature of the coupled system can be exploited for sustaining the bipedalstability of humanoid robots. Also based on this, a marionette-type motion conversion method to adapt captured motions to a humanoid robot is developed owing that there are the differences in capabilities of dynamics and kinematics between a robot and a human. Then this paper discuss on how to stably show human motions with a humanoid robot. We verify that a real humanoid robot can successfully sustain the bipedal stability exhibiting captured whole body motions from various simulations and experiments., Emerging Intelligent Computing Technology and Applications, 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings

We propose a new neural oscillator model to attain rhythmic movements of robotic arms that features enhanced entrainment property. It is known that neural oscillator networks could produce rhythmic commands efficiently and robustly under the changing task environment. However, when a quasi-periodic or non-periodic signal is inputted into the neural oscillator, even the most widely used Matsuoka’s neural oscillator (MNO) may not be entrained to the signal. Therefore, most existing neural oscillator models are only applicable to a particular situation, and if they are coupled to the joints of robotic arms, they may not be capable of achieving human-like rhythmic movement. In this paper, we perform simulations of rotating a crank by a two-link planar arm whose joints are coupled to the proposed entrainment-enhanced neural oscillator (EENO). Specifically, we demonstrate the excellence of EENO and compare it with that of MNO by optimizing their parameters based on simulated annealing (SA). In addition, we show an impressive capability of self-adaptation of EENO that enables the planar arm to make adaptive changes from a circular motion into an elliptical motion. To the authors’ knowledge, this study seems to be the first attempt to enable the oscillator-coupled robotic arm to track a desired trajectory interacting with the environment.

To achieve biologically inspired robot control architectures based on neural oscillator networks, goal-directed imitation is addressed with respect to the problem of motion generation. It would be desirable to easily acquire appropriate motion patterns for skill learning between dissimilar bodies to attain the goal of the demonstrated motion. This requires neural oscillator networks to adapt to the non-periodic nature of arbitrary input patterns exploiting their entrainment properties. However, even in the most widely-used Matsuoka oscillator, when an unknown quasi-periodic or non-periodic signal is applied, its output signal is not always closely entrained. Therefore, current neural oscillator models may not be applied to the proposed goal-directed imitation for skill learning. To solve this problem, a supplementary term is newly included in the equation of Matsuoka oscillator. We verify general properties of the proposed model of the neural oscillator and illustrate in particular its enhanced entrainment by numerical simulation. We also show the possibility of controlling dynamic responses of oscillator-coupled mechanical systems. Technical implications of the results are discussed.

This paper proposes a neural oscillator based control to attain rhythmically dynamic movements of a robot arm. In human or animal, it is known that neural oscillators could produce rhythmic commands efficiently and robustly under the changing task environment. In particular, entrainments of the neural oscillator play a key role to adapt the nervous system to the natural frequency of the interacted environments. Hence, we discuss how a robot arm controls for exhibiting natural adaptive motions as a controller employing the entrainment property. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a real robot arm. Then this work shows the performance of the robot arm coupled to neural oscillators in various tasks that the arm traces a trajectory. Exploiting the neural oscillator and its entrainment property, we experimentally verify an impressive capability of self-adaptation of the neural oscillator that enables the robot arm to make adaptive changes corresponding to an exterior environment.

We address a neural oscillator based control scheme to achieve biologically inspired motion generation. In general, it is known that humans or animals exhibit novel adaptive behaviors regardless of their kinematic configurations against unexpected disturbances or environment changes. This is caused by the entrainment property of the neural oscillator which plays a key role to adapt their nervous system to the natural frequency of the interacted environments. Thus we focus on a self-adapting robot arm control to attain natural adaptive motions as a controller employing the neural oscillator. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a single pendulum coupled with the neural oscillator in simulation and experiment. Then this work shows the performance of the robot arm coupled to neural oscillators in various tasks that the arm traces a trajectory. Exploiting the neural oscillator and its entrainment property, we experimentally verify an impressive capability of biologically inspired self-adaptation behaviors that enables the robot arm to make adaptive changes corresponding to an unexpected environmental variety.

An innovative framework of imitation between dissimilar bodies is proposed to automatically achieve the goal of the perceived behavior. Biologically inspired control based on central pattern generators currently gains increasing attention to embody human-like rhythmic motions to humanoid robots. However, this control approach suffers from highly nonlinear dynamics of neural systems, difficulty of motion pattern generation, uncertainty of behavior between neural systems and biomechanics, and so on. To cope with these problems, the imitation technique is employed in this work. We first propose the self-adjusting adaptor to easily generate an appropriate motion pattern by modifying the perceived motion toward attaining the goal of the behavior. Secondly, we verify the property of entrapment of neural oscillator network in the proposed adaptor to duplicate the regenerated motion pattern. In the numerical simulations of biped locomotion, the perceived pattern data is regenerated to keep the direction of the foot contact force identical between the demonstrator and the imitator Also, the neural oscillator is entrained by external signals under stable conditions. To the best of the authors' knowledge, this paper is the first work to validate the advantages of neural oscillator networks as a tool of imitation