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Movement control mechanism of underwater swimmers via resonance entrainment of central pattern generators Comment on "Control of movement of underwater swimmers: Animals, simulated animates and swimming robots" by Gordleeva et al
The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations.
This paper is devoted to the adaptive synchronization problem in the heterogeneous Hindmarsh–Rose neuronal networks. Heterogeneity is a natural property of biological neuronal networks, as each neuron has its own physiological characteristics, which may differ from other neurons within the population. Therefore, the study of the effect of heterogeneity on the synchronization in the biological neuronal network is an important problem. In order to solve this problem, the ultimate boundedness of the network trajectories is established, and also the limit set is defined for these trajectories. Based on the boundedness analysis and the Speed Gradient method, the adaptive algorithm for adjusting the coupling strength is developed. It is proved mathematically that the developed algorithm provides synchronization in the network under study. The obtained theoretical results are confirmed by the simulations.
Evidence first described in reduced animal models over 100 years ago led to deductions about the control of locomotion through spinal locomotor central pattern generating (CPG) networks. These discoveries in nature were contemporaneous with another form of deductive reasoning found in popular culture-that of Arthur Conan Doyle's detective "Sherlock Holmes". Since the invasive methods used in reduced non-human animal preparations are not amenable to study in humans, we are left instead with deducing from other measures and observations. Using the deductive reasoning approach of Sherlock Holmes as a metaphor for framing research into human CPGs, we speculate and weigh the evidence that should be observable in humans based on knowledge from other species. This review summarizes indirect inference to assess "observable evidence" of pattern generating activity which leads to the logical deduction of CPG contributions to arm and leg activity during locomotion in humans. The question of where a CPG may be housed in the human nervous system remains incompletely resolved at this time. Ongoing understanding, elaboration and application of functioning locomotor CPGs in humans is important for gait rehabilitation strategies in those with neurological injuries.
The mechanism of entrainment to natural oscillations in a class of (bio)mechanical systems described by linear models is investigated. Two new nonlinear control strategies are proposed to achieve global convergence to a prescribed resonance mode of oscillation within a finite time. The effectiveness of the proposed methods for resonance entrainment is demonstrated by examples of computer simulation for linear and nonlinear systems.
Mechanical systems can often be controlled efficiently by exploiting a resonance. An optimal trajectory minimizing an energy cost function is found at (or near) a natural mode of oscillation. Motivated by this fact, we consider the natural entrainment problem: the design of nonlinear feedback controllers for linear mechanical systems to achieve a prescribed mode of natural oscillation for the closed-loop system. We adopt a set of distributed central pattern generators (CPGs) as the basic control architecture, inspired by biological observations. The method of multivariable harmonic balance (MHB) is employed to characterize the condition, approximately, for the closed-loop system to have a natural oscillation as its trajectory. Necessary and sufficient conditions for satisfaction of the MHB equation are derived in the forms useful for control design. It is shown that the essential design freedom can be captured by two parameters, and the design parameter plane can be partitioned into regions, in each of which approximate entrainment to one of the natural modes, with an error bound, is predicted by the MHB analysis. Control mechanisms underlying natural entrainment, as well as limitations and extensions of our results, are discussed.
Rhythmic motor activity requires coordination of different muscles or muscle groups so that they are all active with the same cycle duration and appropriate phase relationships. The neural mechanisms for such phase coupling in vertebrate locomotion are not known. Swimming in the lamprey is accomplished by the generation of a travelling wave of body curvature in which the phase coupling between segments is so controlled as to give approximately one full wavelength on the body at any swimming speed. This article reviews work that has combined mathematical analysis, biological experimentation and computer simulation to provide a conceptual framework within which intersegmental coordination can be investigated. Evidence is provided to suggest that in the lamprey, ascending coupling is dominant over descending coupling and controls the intersegmental phase lag during locomotion. The significance of long-range intersegmental coupling is also discussed.
Central pattern generators are neuronal circuits that when activated can produce rhythmic motor patterns such as walking, breathing, flying, and swimming in the absence of sensory or descending inputs that carry specific timing information. General principles of the organization of these circuits and their control by higher brain centers have come from the study of smaller circuits found in invertebrates. Recent work on vertebrates highlights the importance of neuro-modulatory control pathways in enabling spinal cord and brain stem circuits to generate meaningful motor patterns. Because rhythmic motor patterns are easily quantified and studied, central pattern generators will provide important testing grounds for understanding the effects of numerous genetic mutations on behavior. Moreover, further understanding of the modulation of spinal cord circuitry used in rhythmic behaviors should facilitate the development of new treatments to enhance recovery after spinal cord damage.
The medicinal leech has served as an important experimental preparation for neuroscience research since the late 19th century. Initial anatomical and developmental studies dating back more than 100 years ago were followed by behavioral and electrophysiological investigations in the first half of the 20th century. More recently, intense studies of the neuronal mechanisms underlying leech movements have resulted in detailed descriptions of six behaviors described in this review; namely, heartbeat, local bending, shortening, swimming, crawling, and feeding. Neuroethological studies in leeches are particularly tractable because the CNS is distributed and metameric, with only 400 identifiable, mostly paired neurons in segmental ganglia. An interesting, yet limited, set of discrete movements allows students of leech behavior not only to describe the underlying neuronal circuits, but also interactions among circuits and behaviors. This review provides descriptions of six behaviors including their origins within neuronal circuits, their modification by feedback loops and neuromodulators, and interactions between circuits underlying with these behaviors.
In 1900, Ramón y Cajal advanced the neuron doctrine, defining the neuron as the fundamental signaling unit of the nervous system. Over a century later, neurobiologists address the circuit doctrine: the logic of the core units of neuronal circuitry that control animal behavior. These are circuits that can be called into action for perceptual, conceptual, and motor tasks, and we now need to understand whether there are coherent and overriding principles that govern the design and function of these modules. The discovery of central motor programs has provided crucial insight into the logic of one prototypic set of neural circuits: those that generate motor patterns. In this review, I discuss the mode of operation of these pattern generator networks and consider the neural mechanisms through which they are selected and activated. In addition, I will outline the utility of computational models in analysis of the dynamic actions of these motor networks.
The neuronal circuit controlling the rhythmic movements in animal locomotion is called the central pattern generator (CPG). The biological control mechanism appears to exploit mechanical resonance to achieve efficient locomotion. The objective of this paper is to reveal the fundamental mechanism underlying entrainment of CPGs to resonance through sensory feedback. To uncover the essential principle, we consider the simplest setting where a pendulum is driven by the reciprocal inhibition oscillator. Existence and properties of stable oscillations are examined by the harmonic balance method, which enables approximate but insightful analysis. In particular, analytical conditions are obtained under which harmonic balance predicts existence of an oscillation at a frequency near the resonance frequency. Our result reveals that the resonance entrainment can be maintained robustly against parameter perturbations through two distinct mechanisms: negative integral feedback and positive rate feedback.
Control of movement of underwater swimmers: animals, simulated animates and swimming robots
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