Coordinated movement arises from the interaction of the nervous system, the body, and the environment. The mutual coupling between biomechanics and neural activity has received growing attention in recent years. This has lead to computational models that successfully capture coordinated movement on the level of a whole organism – e.g., crawling, swimming, or postural control. Simultaneously, very significant advances in robotics were inspired by known neural control strategies (e.g., coupled central pattern generators). In addition, recent computational studies have demonstrated how coordinated motor behavior may self-organize from neuromechanical interactions during development. The general principles of how the organization and activity of the nervous system adapts to the mechanics of the body and environment, however, are not yet agreed on.
The workshop brings together researchers with primary background in neuroscience and bio-robotics/neuroengineering, and aims to facilitate a fruitful interaction of approaches from these two fields. The speakers study a wide range of organisms/systems, spanning insects, higher organisms, and humanoid robots. The theoretical concepts and computational methods will provide a unifying framework.
|Stability vs variability of human bipedal standing and walking|
(Georgia State University, Atlanta)
|Cellular mechanisms governing dynamics of Central Pattern Generators|
(University of Rostock)
|Catch me if you can - embodiment of flight control in flies|
(Czech Academy of Sciences)
|Synchronization and frequency tuning in neuromechanical systems|
|Mutual inhibition and co-contraction of a musculoskeletal robot arm
using artificial muscle spindles
|2:10-2:50||Hugo Gravato Marques
(Champalimaud Centre, Lisbon)
|From muscle twitches to coordinated behaviour: a developmental approach|
|Exploring adaptive motor control in locomotion using neuromusculoskeletal models
and legged robots
|4:10-4:30||Panel||Discussion and closing|
Studies of "human motor control" aim to understand how the central nervous system (CNS) controls mechanical dynamics of our body stably and in a robust manner in order to achieve a given motor goal. In this study, we consider a motor strategy called “intermittent control” during human quiet standing and steady-state walking. In particular, we address issues that are related to (1) flexibility of motion in the joints (joint motion with small joint impedance) that can be characterized by movement variability such as postural sway and gait cycle variation, and (2) fractality characterized by power-law distributed long-range correlations in those variations. Our interest is to explore possible mechanisms of how the CNS achieves seemingly contradictory motor-demands, namely, stability and flexibility, simultaneously. We hypothesized that neural control mechanisms, which are capable of compensating delay-induced instability due to signal transmission delay in somatosensory information processing in a robust manner, are responsible for the joint flexibility as well as fractality in movement variability. To examine this hypothesis, we acquired human behavioral data and characterized them quantitatively. We also performed mathematical modeling that could reproduce the behavioral characteristics. We then claim that the intermittent control might be a promising strategy that can achieve stability and flexibility simultaneously. In this study, we consider a hypothetical motor strategy called “intermittent control” during human quiet standing and steady-state walking. In particular, we address issues that are related to (1) joint flexibility characterized by movement variability in postural sway and gait cycle variation, and (2) fractality characterized by power-law distributed long-range correlations in those variations. Possible neural control mechanisms that are responsible for generating joint flexibity and fractality in movement variability should be capable of compensating delay-induced instability due to signal trasmission delay in somatosensory information processing in a robust manner. We claim that the intermittent control might be a promissing strategy that can achieve seemingly contradictory motor-demands, namely, stability and flexibility, simultaneously.
The dynamics of individual neurons are crucial for producing functional activity in neuronal circuits. Dynamical systems theory provides a framework to describe the activity of neuronal systems. We describe a family of mechanisms that control transient and oscillatory neuronal activity. These mechanisms are organized around the cornerstone bifurcation, which satisfies the criteria for both the saddle-node bifurcation on invariant circle (SNIC) and the blue sky catastrophe. The first mechanism describes control over the burst duration and interburst interval of an endogenously bursting neuron. The second mechanism provides control of the duration of an evoked burst in an endogenously silent neuron. The third mechanism determines the delay to spiking after inhibition in an endogenously spiking neuron.
We describe how these mechanisms could explain basic dynamics of a central pattern generator (CPG) that controls six-legged locomotion [Barnett and Cymbalyuk, PLOS ONE 2014]. The network generated travelling waves of activity that propagated from posterior to anterior segments. We applied the mechanism that controls the duration of an evoked burst in a silent neuron to control gait in this CPG. By controlling the duration of evoked bursts in interneurons that controlled retraction, we demonstrated control over the period and duration of retraction in the network. These mechanisms were used to control smooth transitions between tripod and metachronal-wave gaits. The described mechanisms are generic and could be applied to a wide range of problems in motor control.
The impressive aerial performance of flies is based on a highly specialized flight apparatus, including a phase-coded low level flight control circuit. In my talk, I will present experimental results highlighting the importance of an integrative approach for a comprehensive understanding of flight control in flies. First, I will show how considering biomechanical flight muscles properties improves our understanding of neural sensory integration mechanisms. Second, I will discuss wingbeat rhythm generation by the interaction of neuromechanics and environment in the framework of nonlinear dynamics systems.
To achieve efficient motion, the pattern of motor activation needs to be properly matched with the mechanical properties of the body. For example, afferent feedback permits some neural central pattern generators to adjust their rhythm to the mechanical resonance frequency of a limb, thus achieving maximum amplitude of motion. It is an open question if such “tuning to mechanical resonance” can be considered a generic property of neuromechanical systems. In the first part of the talk, I will describe modeling based on experiments in which we recorded the motion of tethered flying fruit flies, and demonstrated that the dynamical patterns of wing movement depend on the mechanical properties of the tether. We analyze these patterns using a minimalistic model based on two coupled oscillators – a nonlinear oscillator of myogenic origin and a damped oscillator representing the tether. Both the experiment and the model show robust tuning to the tether resonance frequency. The coupling of the tether to the wing oscillator is effected through the haltere mechanosensory feedback. It is remarkable that this feedback, which serves as a stabilizer during free flight, leads on the contrary to motion with maximum amplitude in the case of tethered flight.
In the second part of the talk, I will discuss the bilateral coherence of hand tremor in human subjects. Our accelerometric recordings and computational analysis reveal surprisingly coherent motion of the left and right hand, both in healthy subjects and in patients with diagnosed essential tremor. I will discuss the character of this synchrony and the likely underlying physiological mechanisms.
Achieving adaptive movements comparable to those of humans is one of the grand challenges of robotics. Thus far, many musculoskeletal robots have been developed toward this challenge. Adaptability of those robots is, however, still inferior to that of humans even though their developers have tried to mimic human musculoskeletal structure. One of the reasons is attributed to the fact that the control methods for musculoskeletal robots are not yet well established. In contrast, humans achieve adaptive movement by controlling their extremely complex musculoskeletal systems, and spinal cords have been known to provide reflexes as important local controllers. In this study, we focus on two types of distinguishing muscular drives: mutual inhibition and co-contraction. To this end, we adopt a constructive approach with real robots to clarify the generating mechanism of the muscular drives and establish a control method for musculoskeletal robots driven by antagonistic muscles. As a consequence, we show that adaptively generating and switching the two types of muscular derives can be realized at the level of the spinal cord reflex.
The holistic nature of animal behaviour often requires the brain to coordinate the movements of the entire body in space and time, yet the neural mechanisms supporting body coordination are poorly understood. In this talk I will propose two mechanisms for the development of motor coordination. First, I will show that the sensorimotor information induced by muscle twitches is sufficient to self-organize reflex circuits similar to those in the mammalian spinal cord (in three different sensory modalities). Second, I will show that the self-organized reflex structures can form muscle synergies that are mechanically grounded and thus can be naturally exploited to coordinate the temporal activity of several muscles. This work has been carried out in real (robotic) as well as simulated musculoskeletal systems.
Humans and animals produce adaptive walking in diverse environments by skillfully manipulating their complicated and redundant musculoskeletal systems. To elucidate their adaptation mechanism, split-belt treadmills, which have two parallel belts, have been used. During such split-belt treadmill walking, two types of adaptations have been identified: early and late. Early-type adaptations appear as rapid changes in interlimb and intralimb coordination when the belt speed condition changes between tied and split-belt configurations. In contrast, late-type adaptations occur after the early-type adaptations as a gradual change and only involve interlimb coordination. It has been suggested that these adaptations are governed primarily by the spinal cord and cerebellum, but the underlying mechanism remains unclear. To reveal the adaptation mechanism, we developed a biped robot and its control system composed of spinal and cerebellar models. We also constructed a neuromusculoskeletal model for split-belt treadmill walking by hindlimbs of rats. We investigated adaptive behavior and its mechanism using computer simulations and robot experiments. The results were evaluated in comparison with measured data during split-belt treadmill walking of humans and rats, and the adaptation mechanism is discussed from a dynamic viewpoint.