From neuron to behavior: dynamic equation-based prediction of biological processes in motor control

Daun-Gruhn, Silvia; Büschges, Ansgar

doi:10.1007/s00422-011-0446-6

Cited by 42 publications

(41 citation statements)

References 130 publications

(163 reference statements)

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“…From a control perspective, past studies have intensively argued mainly from the viewpoint of two distinct control paradigms: chains of reflexes (Cruse, 1983, 1990; Cruse et al, 1998; Dürr et al, 2004; Schilling et al, 2013) and CPGs (Pearson and Iles, 1973; Bässler and Wegner, 1983; Bässler, 1986, 1993; Ryckebusch and Laurent, 1993; Büschges et al, 1995, 2004; Bässler and Büschges, 1998; Büschges, 2005; Borgmann et al, 2009; Daun-Gruhn and Büschges, 2011; Marder and Bucher, 2011). In the chain-of-reflex approach, a control system is modeled by using many chained discontinuous reflexive events, in which locomotion can be generated purely from the interaction between sensory feedback signals and the body.…”

Section: Methodsmentioning

confidence: 99%

“…In the chain-of-reflex approach, a control system is modeled by using many chained discontinuous reflexive events, in which locomotion can be generated purely from the interaction between sensory feedback signals and the body. However, the discontinuity in this approach may impede mathematical tractability (Daun-Gruhn and Büschges, 2011). In contrast, in the CPG approach, a control system is modeled by using directly coupled oscillators to generate feedforward motor commands, based on a continuous dynamical system, i.e., a set of differential equations, for the interlimb coordination.…”

Section: Methodsmentioning

confidence: 99%

“…Aiming to elucidate the mechanism responsible for the interlimb coordination in hexapedal locomotion, various studies have been conducted to date by focusing on specific insects, e.g., stick insects (Graham, 1972, 1977; Cruse, 1976; Foth and Graham, 1983a,b; Dean, 1991; Grabowska et al, 2012) and cockroaches (Hughes, 1957; Pearson and Iles, 1969; Noah et al, 2004; Goldman et al, 2006; Sponberg and Full, 2008) and/or by focusing on control paradigms, e.g., central pattern generators (CPGs) (Pearson and Iles, 1973; Bässler and Wegner, 1983; Bässler, 1986, 1993; Ryckebusch and Laurent, 1993; Büschges et al, 1995, 2004; Bässler and Büschges, 1998; Büschges, 2005; Borgmann et al, 2009; Daun-Gruhn and Büschges, 2011; Marder and Bucher, 2011) and chains of reflexes (Cruse, 1983, 1990; Cruse et al, 1998; Dürr et al, 2004; Schilling et al, 2013). The knowledge obtained from these past studies deepened biological understanding of the interlimb coordination mechanism greatly; however, the diversity of these approaches may have confused roboticists who want to build adaptive insect-like hexapod robots via bio-inspired approaches (Kimura et al, 1993; Beer et al, 1997; Altendorfer et al, 2001; Ritzmann et al, 2004; Ambe et al, 2013; Manoonpong et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

et al. 2017

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Insects exhibit adaptive and versatile locomotion despite their minimal neural computing. Such locomotor patterns are generated via coordination between leg movements, i.e., an interlimb coordination, which is largely controlled in a distributed manner by neural circuits located in thoracic ganglia. However, the mechanism responsible for the interlimb coordination still remains elusive. Understanding this mechanism will help us to elucidate the fundamental control principle of animals' agile locomotion and to realize robots with legs that are truly adaptive and could not be developed solely by conventional control theories. This study aims at providing a “minimal" model of the interlimb coordination mechanism underlying hexapedal locomotion, in the hope that a single control principle could satisfactorily reproduce various aspects of insect locomotion. To this end, we introduce a novel concept we named “Tegotae,” a Japanese concept describing the extent to which a perceived reaction matches an expectation. By using the Tegotae-based approach, we show that a surprisingly systematic design of local sensory feedback mechanisms essential for the interlimb coordination can be realized. We also use a hexapod robot we developed to show that our mathematical model of the interlimb coordination mechanism satisfactorily reproduces various insects' gait patterns.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

et al. 2017

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“…For this, we use multiple CPG modules inspired by the 3-CPGs model proposed by Daun-Gruhn et al [11] [12]. We extend our previously proposed chaotic CPG controller to three CPGs, which include one master-CPG and two clientCPGs.…”

Section: Introductionmentioning

confidence: 98%

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

Ren

Chen

Kolodziejski

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In chaos control, an originally chaotic system is modified so that periodic dynamics arise. One application of this is to use the periodic dynamics of a single chaotic system as walking patterns in legged robots. In our previous work we applied such a controlled chaotic system as a central pattern generator (CPG) to generate different gait patterns of our hexapod robot AMOSII. However, if one or more legs break, its control fails. Specifically, in the scenario presented here, its movement permanently deviates from a desired trajectory. This is in contrast to the movement of real insects as they can compensate for body damages, for instance, by adjusting the remaining legs' frequency. To achieve this for our hexapod robot, we extend the system from one chaotic system serving as a single CPG to multiple chaotic systems, performing as multiple CPGs. Without damage, the chaotic systems synchronize and their dynamics is identical (similar to a single CPG). With damage, they can lose synchronization leading to independent dynamics. In both simulations and real experiments, we can tune the oscillation frequency of every CPG manually so that the controller can indeed compensate for leg damage. In comparison to the trajectory of the robot controlled by only a single CPG, the trajectory produced by multiple chaotic CPG controllers resembles the original trajectory by far better. Thus, multiple chaotic systems that synchronize for normal behavior but can stay desynchronized in other circumstances are an effective way to control complex behaviors where, for instance, different body parts have to do independent movements like after leg damage.

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“…These interactions are essential for the processing of sensory feedback signals. Leg joints are driven by their individual pattern generating networks (CPGs; Akay et al 2004;Büschges 1995Büschges , 2005Daun-Gruhn 2011;Ekeberg et al 2004), which can be controlled by sensory input, for example, by load signals from the campaniform sensilla Daun-Gruhn et al 2011;Zill et al 2004Zill et al , 2009Zill et al , 2011; for a review, see Büschges and Gruhn 2008). The coordination between leg joints has been investigated on the behavioral level (Cruse 1990;Graham 1972;von Buddenbrock 1921;Wendler 1965Wendler , 1978 as well as on the neuromuscular level by recording EMG activity and intracellular and extracellular electrical activity of motoneurons (MNs; Büschges 1995;Büschges et al 2004Ritzmann and Büschges 2007;Rosenbaum et al 2010).…”

mentioning

confidence: 99%

A neuromechanical model for the neuronal basis of curve walking in the stick insect

Knops

Tóth

Guschlbauer

et al. 2013

Journal of Neurophysiology

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The coordination of the movement of single and multiple limbs is essential for the generation of locomotion. Movement about single joints and the resulting stepping patterns are usually generated by the activity of antagonistic muscle pairs. In the stick insect, the three major muscle pairs of a leg are the protractor and retractor coxae, the levator and depressor trochanteris, and the flexor and extensor tibiae. The protractor and retractor move the coxa, and thereby the leg, forward and backward. The levator and depressor move the femur up and down. The flexor flexes, and the extensor extends the tibia about the femur-tibia joint. The underlying neuronal mechanisms for a forward stepping middle leg have been thoroughly investigated in experimental and theoretical studies. However, the details of the neuronal and mechanical mechanisms driving a stepping single leg in situations other than forward walking remain largely unknown. Here, we present a neuromechanical model of the coupled three joint control system of the stick insect's middle leg. The model can generate forward, backward, or sideward stepping. Switching between them is achieved by changing only a few central signals controlling the neuromechanical model. In kinematic simulations, we are able to generate curve walking with two different mechanisms. In the first, the inner middle leg is switched from forward to sideward and in the second to backward stepping. Both are observed in the behaving animal, and in the model and animal alike, backward stepping of the inner middle leg produces tighter turns than sideward stepping.

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From neuron to behavior: dynamic equation-based prediction of biological processes in motor control

Cited by 42 publications

References 130 publications

A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

A neuromechanical model for the neuronal basis of curve walking in the stick insect

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