Coordination of the legs of a slow-walking cat

Cruse, Holk; Warnecke, H.

doi:10.1007/bf00229012

Cited by 38 publications

(21 citation statements)

References 17 publications

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“…WALKNET is based on the behavioural rules that have been derived from the experimental studies on walking stick insects. Similar rules have been found for the walking crayfish (Cruse 1990) and cats (Cruse & Warnecke 1992). The core of WALKNET consists of a set of six single-leg controllers, each of which is built by a number of distinct modules that are responsible for solving particular subtasks.…”

Section: (D ) Behaviour Is Investigated First Followed By Simulationmentioning

confidence: 82%

Insect walking is based on a decentralized architecture revealing a simple and robust controller

Cruse

Dürr

Schmitz

2006

Phil. Trans. R. Soc. A.

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Control of walking in rugged terrain requires one to incorporate different issues, such as the mechanical properties of legs and muscles, the neuronal control structures for the single leg, the mechanics and neuronal control structures for the coordination between legs, as well as central decisions that are based on external information and on internal states. Walking in predictable environments and fast running, to a large degree, rely on muscle mechanics. Conversely, slow walking in unpredictable terrain, e.g. climbing in rugged structures, has to rely on neuronal systems that monitor and intelligently react to specific properties of the environment. An arthropod model system that shows the latter abilities is the stick insect, based on which this review will be focused. An insect, when moving its six legs, has to control 18 joints, three per leg, and therefore has to control 18 degrees of freedom (d.f.). As the body position in space is determined by 6 d.f. only, there are 12 d.f. open to be selected. Therefore, a fundamental problem is as to how these extra d.f. are controlled. Based mainly on behavioural experiments and simulation studies, but also including neurophysiological results, the following control structures have been revealed. Legs act as basically independent systems. The quasi-rhythmic movement of the individual leg can be described to result from a structure that exploits mechanical coupling of the legs via the ground and the body. Furthermore, neuronally mediated influences act locally between neighbouring legs, leading to the emergence of insect-type gaits. The underlying controller can be described as a free gait controller. Cooperation of the legs being in stance mode is assumed to be based on mechanical coupling plus local positive feedback controllers. These controllers, acting on individual leg joints, transform a passive displacement of a joint into an active movement, generating synergistic assistance reflexes in all mechanically coupled joints. This architecture is summarized in the form of the artificial neural network, Walknet, that is heavily dependent on sensory feedback at the proprioceptive level. Exteroceptive feedback is exploited for global decisions, such as the walking direction and velocity.

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Section: (D ) Behaviour Is Investigated First Followed By Simulationmentioning

confidence: 82%

Insect walking is based on a decentralized architecture revealing a simple and robust controller

Cruse

Dürr

Schmitz

2006

Phil. Trans. R. Soc. A.

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“…This effect has been documented in curvewalking stick insects (Jander, 1985). Similarly, the strong contralateral coupling according to rule 1 between hind legs in walking cats (Cruse and Warnecke, 1992) is probably mostly due to unilateral load-dependent feedback from Golgi tendon organs that prevent flexion and, therefore, lift-off (reviewed by Pearson, 1995). In stick insects, the centre of gravity lies behind the hind leg coxae, so the swing movements of a hind leg cause increased loading of the ipsilateral middle leg, though to a lesser extent than of the contralateral hind leg.…”

Section: Differences Between Leg Pairsmentioning

confidence: 98%

Context-dependent changes in strength and efficacy of leg coordination mechanisms

Dürr

2005

Journal of Experimental Biology

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Appropriate coordination of stepping in adjacent legs is crucial for stable walking. Several leg coordination rules have been derived from behavioural experiments on walking insects, some of which also apply to arthropods with more than six legs and to four-legged walking vertebrates. Three of these rules affect the timing of stance-swing transition [rules 1 to 3 (sensu Cruse)]. They can give rise to normal leg coordination and adaptive responses to disturbances, as shown by kinematic simulations and dynamic hardware tests. In spite of their importance to the study of animal walking, the coupling strength associated with these rules has never been measured experimentally. Generally coupling strength of the underlying mechanisms has been considered constant rather than context-dependent.The present study analyses stepping patterns of the stick insect Carausius morosus during straight and curve walking sequences. To infer strength and efficacy of coupling between pairs of sender and receiver legs, the likelihood of the receiver leg being in swing is determined, given a certain delay relative to the time of a swing-stance (or stance-swing) transition in the sender leg. This is compared to a corresponding measure for independent, hence uncoupled, step sequences. The difference is defined as coupling strength. The ratio of coupling strength and its theoretical maximum is defined as efficacy.Irrespective of the coordination rule, coupling strength between ipsilateral leg pairs is at least twice that of contralateral leg pairs, being strongest between ipsilateral hind and middle legs and weakest between contralateral middle legs. Efficacy is highest for inhibitory rule 1, reaching 84-95% for ipsilateral and 29-65% for contralateral leg pairs. Efficacy of excitatory rules 2 and 3 ranges between 35-56% for ipsilateral and 8-21% for contralateral leg pairs. The behavioural transition from straight to curve walking is associated with contextdependent changes in coupling strength, increasing in both outer leg pairs and decreasing between inner hind and middle leg. Thus, the coordination rules that are thought to underlie many adaptive properties of the walking system, themselves adapt in a context-dependent manner.

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“…Termination of locomotion could be achieved by simply removing the excitatory input to this region [20][21][22]. Further support for the importance of interlimb coordination was obtained by Cruse and Warnecke (1992) [23], in the intact cat and by Giuliani and Smith (1987) [24], in the chronic spinal cat, Th latter authors found that the coupling between hind leg movements during stepping in the air was weaker fol lowing deafferentation of a hindlimb. They showed that during the majority of locomotor movements, the bilat eral stepping was characterized by irregular phasing, with the intact hindlimb stepping at a faster frequency than the deafferented leg.…”

Section: Evidence For Cpg In Catmentioning

confidence: 99%

Neural control of locomotion; Part 1: The central pattern generator from cats to humans

1998

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In the last years it has become possible to regain some locomotor activity in patients suffering from an incomplete spinal cord injury (SCI) through intense training on a treadmill. The ideas behind this approach owe much to insights derived from animal studies. Many studies showed that cats with complete spinal cord transection can recover locomotor function. These observations were at the basis of the concept of the central pattern generator (CPG) located at spinal level. The evidence for such a spinal CPG in cats and primates (including man) is reviewed in part 1, with special emphasis on some very recent developments which support the view that there is a human spinal CPG for locomotion.

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Coordination of the legs of a slow-walking cat

Cited by 38 publications

References 17 publications

Insect walking is based on a decentralized architecture revealing a simple and robust controller

Insect walking is based on a decentralized architecture revealing a simple and robust controller

Context-dependent changes in strength and efficacy of leg coordination mechanisms

Neural control of locomotion; Part 1: The central pattern generator from cats to humans

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