Motor control of an insect leg during level and incline walking

During walking, the leg motor system must continually adjust to changes in mechanical conditions, such as the inclination of the ground. To understand the underlying control, it is important to know how changes in leg muscle activity relate to leg kinematics (movements) and leg dynamics (forces, torques). Here, we studied these parameters in hindlegs of stick insects (Carausius morosus) during level and uphill/downhill (±45 deg) walking, using a combination of electromyography, 3D motion capture and ground reaction force measurements. We find that some kinematic parameters including leg joint angles and body height vary across walking conditions. However, kinematics vary little compared with dynamics: horizontal leg forces and torques at the thorax-coxa joint (leg protraction/retraction) and femur-tibia joint (leg flexion/extension) tend to be stronger during uphill walking and are reversed in sign during downhill walking. At the thorax-coxa joint, the different mechanical demands are met by adjustments in the timing and magnitude of antagonistic muscle activity. Adjustments occur primarily in the first half of stance after the touch-down of the leg. When insects transition from level to incline walking, the characteristic adjustments in muscle activity occur with the first step of the leg on the incline, but not in anticipation. Together, these findings indicate that stick insects adjust leg muscle activity on a step-by-step basis so as to maintain a similar kinematic pattern under different mechanical demands. The underlying control might rely primarily on feedback from leg proprioceptors signaling leg position and movement.

Force dynamics and synergist muscle activation in stick insects: the effects of using joint torques as mechanical stimuli

Many sensory systems are tuned to specific parameters of behaviors and have effects that are task-specific. We have studied how force feedback contributes to activation of synergist muscles in serially homologous legs of stick insects. Forces were applied using conventional half-sine or ramp and hold functions. We also utilized waveforms of joint torques calculated from experiments in freely walking animals. In all legs, forces applied to either the tarsus (foot) or proximal leg segment (trochanter) activated synergist muscles that generate substrate grip and support, but coupling of the depressor muscle to tarsal forces was weak in the front legs. Activation of trochanteral receptors using ramp and hold functions generated positive feedback to the depressor muscle in all legs when animals were induced to seek substrate grip. However, discharges of the synergist flexor muscle showed adaptation at moderate force levels. In contrast, application of forces using torque waveforms, which do not have a static hold phase, produced sustained discharges in muscle synergies with little adaptation. Firing frequencies reflected the magnitude of ground reaction forces, were graded to changes in force amplitude, and could also be modulated by transient force perturbations added to the waveforms. Comparison of synergist activation by torques and ramp and hold functions revealed a strong influence of force dynamics (dF/d t). These studies support the idea that force receptors can act to tune muscle synergies synchronously to the range of force magnitudes and dynamics that occur in each leg according to their specific use in behavior. NEW & NOTEWORTHY The effects of force receptors (campaniform sensilla) on leg muscles and synergies were characterized in stick insects using both ramp and hold functions and waveforms of joint torques calculated by inverse dynamics. Motor responses were sustained and showed reduced adaptation to the more "natural" and nonlinear torque stimuli. Calculation of the first derivative (dF/d t) of the torque waveforms demonstrated that this difference was correlated with the dynamic sensitivities of the system.

A load-based mechanism for inter-leg coordination in insects

Animals rely on an adaptive coordination of legs during walking. However, which specific mechanisms underlie coordination during natural locomotion remains largely unknown. One hypothesis is that legs can be coordinated mechanically based on a transfer of body load from one leg to another. To test this hypothesis, we simultaneously recorded leg kinematics, ground reaction forces and muscle activity in freely walking stick insects (Carausius morosus). Based on torque calculations, we show that load sensors (campaniform sensilla) at the proximal leg joints are well suited to encode the unloading of the leg in individual steps. The unloading coincides with a switch from stance to swing muscle activity, consistent with a load reflex promoting the stance-to-swing transition. Moreover, a mechanical simulation reveals that the unloading can be ascribed to the loading of a specific neighbouring leg, making it exploitable for inter-leg coordination. We propose that mechanically mediated load-based coordination is used across insects analogously to mammals.

A Synthetic Nervous System Controls a Simulated Cockroach

The purpose of this work is to better understand how animals control locomotion. This knowledge can then be applied to neuromechanical design to produce more capable and adaptable robot locomotion. To test hypotheses about animal motor control, we model animals and their nervous systems with dynamical simulations, which we call synthetic nervous systems (SNS). However, one major challenge is picking parameter values that produce the intended dynamics. This paper presents a design process that solves this problem without the need for global optimization. We test this method by selecting parameter values for SimRoach2, a dynamical model of a cockroach. Each leg joint is actuated by an antagonistic pair of Hill muscles. A distributed SNS was designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint’s controller was designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization. Once tuned, SimRoach2 walks through a simulated environment, with several cockroach-like features. A model with such reliable low-level performance is necessary to investigate more sophisticated locomotion patterns in the future.

Fiber-type distribution in insect leg muscles parallels similarities and differences in the functional role of insect walking legs

Previous studies have demonstrated that myofibrillar ATPase (mATPase) enzyme activity in muscle fibers determines their contraction properties. We analyzed mATPase activities in muscles of the front, middle and hind legs of the orthopteran stick insect (Carausius morosus) to test the hypothesis that differences in muscle fiber types and distributions reflected differences in their behavioral functions. Our data show that all muscles are composed of at least three fiber types, fast, intermediate and slow, and demonstrate that: (1) in the femoral muscles (extensor and flexor tibiae) of all legs, the number of fast fibers decreases from proximal to distal, with a concomitant increase in the number of slow fibers. (2) The swing phase muscles protractor coxae and levator trochanteris, have smaller percentages of slow fibers compared to the antagonist stance muscles retractor coxae and depressor trochanteris. (3) The percentage of slow fibers in the retractor coxae and depressor trochanteris increases significantly from front to hind legs. These results suggest that fiber-type distribution in leg muscles of insects is not identical across leg muscles but tuned towards the specific function of a given muscle in the locomotor system.

Neuroethological Concepts and their Transfer to Walking Machines

A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences of the various output parameters are comprised by the weight matrix of the net. Based upon behavioral observations of insect walking, we further demonstrate how a decentralized network of separate modules, each one described by an ANN, can account for adaptive behavior. Complex coordination patterns of several manipulators are controlled by imposing simple interaction rules between limbs, and by exploiting the interaction of the body with its physical environment. Finally, we discuss the technical use of leg-like active tactile sensors for obstacle detection, and we show how specific design of such active sensors may increase efficiency of walking on rough terrain. Applied to active sensors, an example of parallel, self-organizing forward models on the basis of extended Kohonen maps is presented to emphasize the potential of adaptive forward models in motor control.

Neuromechanical model of praying mantis explores the role of descending commands in pre-strike pivots

Praying mantises hunt by standing on their meso- and metathoracic legs and using them to rotate and translate (together, 'pivot') their bodies toward prey. We have developed a neuromechanical software model of the praying mantis Tenodera sinensis to use as a platform for testing postural controllers that the animal may use while hunting. Previous results showed that a feedforward model was insufficient for capturing the diversity of posture observed in the animal (Szczecinski et al 2014 Biomimetic and Biohybrid Syst. 3 296-307). Therefore we have expanded upon this model to make a flexible controller with feedback that more closely mimics the animal. The controller actuates 24 joints in the legs of a dynamical model to orient the head and translate the thorax toward prey. It is controlled by a simulation of nonspiking neurons assembled as a highly simplified version of networks that may exist in the mantid central complex and thoracic ganglia. Because of the distributed nature of these networks, we hypothesize that descending commands that orient the mantis toward prey may be simple direction-of-intent signals, which are turned into motor commands by the structure of low-level networks in the thoracic ganglia. We verify this through a series of experiments with the model. It captures the speed and range of mantid pivots as reported in other work (Yamawaki et al 2011 J. Insect Physiol. 57 1010-6). It is capable of pivoting toward prey from a variety of initial postures, as seen in the animal. Finally, we compare the model's joint kinematics during pivots to preliminary 3D kinematics collected from Tenodera.

The role of leg touchdown for the control of locomotor activity in the walking stick insect

Much is known on how select sensory feedback contributes to the activation of different motoneuron pools in the locomotor control system of stick insects. However, even though activation of the stance phase muscles depressor trochanteris, retractor unguis, flexor tibiae and retractor coxae is correlated with the touchdown of the leg, the potential sensory basis of this correlation or its connection to burst intensity remains unknown. In our experiments, we are using a trap door setup to investigate how ground contact contributes to stance phase muscle activation and burst intensity in different stick insect species, and which afferent input is involved in the respective changes. While the magnitude of activation is changed in all of the above stance phase muscles, only the timing of the flexor tibiae muscle is changed if the animal unexpectedly steps into a hole. Individual and combined ablation of different force sensors on the leg demonstrated influence from femoral campaniform sensilla on flexor muscle timing, causing a significant increase in the latencies during control and air steps. Our results show that specific load feedback signals determine the timing of flexor tibiae activation at the swing-to-stance transition in stepping stick insects, but that additional feedback may also be involved in flexor muscle activation during stick insect locomotion. With respect to timing, all other investigated stance phase muscles appear to be under sensory control other than that elicited through touchdown.

Walknet, a bio-inspired controller for hexapod walking

Walknet comprises an artificial neural network that allows for the simulation of a considerable amount of behavioral data obtained from walking and standing stick insects. It has been tested by kinematic and dynamic simulations as well as on a number of six-legged robots. Over the years, various different expansions of this network have been provided leading to different versions of Walknet. This review summarizes the most important biological findings described by Walknet and how they can be simulated. Walknet shows how a number of properties observed in insects may emerge from a decentralized architecture. Examples are the continuum of so-called "gaits," coordination of up to 18 leg joints during stance when walking forward or backward over uneven surfaces and negotiation of curves, dealing with leg loss, as well as being able following motion trajectories without explicit precalculation. The different Walknet versions are compared to other approaches describing insect-inspired hexapod walking. Finally, we briefly address the ability of this decentralized reactive controller to form the basis for the simulation of higher-level cognitive faculties exceeding the capabilities of insects.

Force encoding in stick insect legs delineates a reference frame for motor control

The regulation of forces is integral to motor control. However, it is unclear how information from sense organs that detect forces at individual muscles or joints is incorporated into a frame of reference for motor control. Campaniform sensilla are receptors that monitor forces by cuticular strains. We studied how loads and muscle forces are encoded by trochanteral campaniform sensilla in stick insects. Forces were applied to the middle leg to emulate loading and/or muscle contractions. Selective sensory ablations limited activities recorded in the main leg nerve to specific receptor groups. The trochanteral campaniform sensilla consist of four discrete groups. We found that the dorsal groups (Groups 3 and 4) encoded force increases and decreases in the plane of movement of the coxo-trochanteral joint. Group 3 receptors discharged to increases in dorsal loading and decreases in ventral load. Group 4 showed the reverse directional sensitivities. Vigorous, directional responses also occurred to contractions of the trochanteral depressor muscle and to forces applied at the muscle insertion. All sensory discharges encoded the amplitude and rate of loading or muscle force. Stimulation of the receptors produced reflex effects in the depressor motoneurons that could reverse in sign during active movements. These data, in conjunction with findings of previous studies, support a model in which the trochanteral receptors function as an array that can detect forces in all directions relative to the intrinsic plane of leg movement. The array could provide requisite information about forces and simplify the control and adaptation of posture and walking.

Encoding of force increases and decreases by tibial campaniform sensilla in the stick insect, Carausius morosus

Detection of force increases and decreases is important in motor control. Experiments were performed to characterize the structure and responses of tibial campaniform sensilla, receptors that encode forces through cuticular strains, in the middle leg of the stick insect (Carausius morosus). The sensilla consist of distinct subgroups. Group 6A sensilla are located 0.3 mm distal to the femoro-tibial joint and have oval shaped cuticular caps. Group 6B receptors are 1 mm distal to the joint and have round caps. All sensilla show directional, phasico-tonic responses to forces applied to the tibia in the plane of joint movement. Group 6B sensilla respond to force increases in the direction of joint extension while Group 6A receptors discharge when those forces decrease. Forces applied in the direction of joint flexion produce the reverse pattern of sensory discharge. All receptors accurately encode the rate of change of force increments and decrements. Contractions of tibial muscles also produce selective, directional sensory discharges. The subgroups differ in their reflex effects: Group 6B receptors excite and Group 6A sensilla inhibit tibial extensor and trochanteral depressor motoneurons. The tibial campaniform sensilla can, therefore, encode force increases or decreases and aid in adapting motor outputs to changes in load.

Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller

This article presents modular recurrent neural network controllers for single legs of a biomimetic six-legged robot equipped with standard DC motors. Following arguments of Ekeberg et al. (Arthropod Struct Dev 33:287-300, 2004), completely decentralized and sensori-driven neuro-controllers were derived from neuro-biological data of stick-insects. Parameters of the controllers were either hand-tuned or optimized by an evolutionary algorithm. Employing identical controller structures, qualitatively similar behaviors were achieved for robot and for stick insect simulations. For a wide range of perturbing conditions, as for instance changing ground height or up- and downhill walking, swing as well as stance control were shown to be robust. Behavioral adaptations, like varying locomotion speeds, could be achieved by changes in neural parameters as well as by a mechanical coupling to the environment. To a large extent the simulated walking behavior matched biological data. For example, this was the case for body support force profiles and swing trajectories under varying ground heights. The results suggest that the single-leg controllers are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.

Activity Patterns and Timing of Muscle Activity in the Forward Walking and Backward Walking Stick Insect Carausius morosus

Understanding how animals control locomotion in different behaviors requires understanding both the kinematics of leg movements and the neural activity underlying these movements. Stick insect leg kinematics differ in forward and backward walking. Describing leg muscle activity in these behaviors is a first step toward understanding the neuronal basis for these differences. We report here the phasing of EMG activities and latencies of first spikes relative to precise electrical measurements of middle leg tarsus touchdown and liftoff of three pairs ( protractor/retractor coxae, levator/depressor trochanteris, extensor/flexor tibiae) of stick insect middle leg antagonistic muscles that play central roles in generating leg movements during forward and backward straight walking. Forward walking stance phase muscle (depressor, flexor, and retractor) activities were tightly coupled to touchdown, beginning on average 93 ms prior to and 9 and 35 ms after touchdown, respectively. Forward walking swing phase muscle (levator, extensor, and protractor) activities were less tightly coupled to liftoff, beginning on average 100, 67, and 37 ms before liftoff, respectively. In backward walking the protractor/retractor muscles reversed their phasing compared with forward walking, with the retractor being active during swing and the protractor during stance. Comparison of intact animal and reduced two- and one-middle-leg preparations during forward straight walking showed only small alterations in overall EMG activity but changes in first spike latencies in most muscles. Changing body height, most likely due to changes in leg joint loading, altered the intensity, but not the timing, of depressor muscle activity.

A dynamic model of thoracic differentiation for the control of turning in the stick insect

Leg movements of stick insects (Carausius morosus) making turns towards visual targets are examined in detail, and a dynamic model of this behaviour is proposed. Initial results suggest that front legs shape most of the body trajectory, while the middle and hind legs just follow external forces (Rosano H, Webb B, in The control of turning in real and simulated stick insects, vol. 4095, pp 145-156, 2006). However, some limitations of this explanation and dissimilarities in the turning behaviour of the insect and the model were found. A second set of behavioural experiments was made by blocking front tarsi to further investigate the active role of the other legs for the control of turning. The results indicate that it is necessary to have different roles for each pair of legs to replicate insect behaviour. We demonstrate that the rear legs actively rotate the body while the middle legs move sideways tangentially to the hind inner leg. Furthermore, we show that on average the middle inner and hind outer leg contribute to turning while the middle outer leg and hind inner leg oppose body rotation. These behavioural results are incorporated into a 3D dynamic robot simulation. We show that the simulation can now replicate more precisely the turns made by the stick insect.

Tuning posture to body load: decreases in load produce discrete sensory signals in the legs of freely standing cockroaches

Decreases in load are important cues in the control of posture and walking. We recorded activities of the tibial campaniform sensilla, receptors that monitor forces as strains in the exoskeleton, in the middle legs of freely moving cockroaches. Small magnets were attached to the thorax and body load was changed by applying currents to a coil below the substrate. Body position was monitored by video recording. The tibial sensilla are organized into proximal and distal subgroups that have different response properties and reflex effects: proximal sensilla excite extensor motoneurons while distal receptors inhibit extensor firing. Sudden load decreases elicited bursts from distal sensilla, while increased load excited proximal receptors. The onset of sensory discharges closely approximated the time of peak velocity of body movement in both load decreases and increases. Firing of distal sensilla rapidly adapted to sustained unloading, while proximal sensilla discharged tonically to load increases. Load decreases of small amplitude or at low rates produced only inhibition of proximal activity while decrements of larger size or rate elicited distal firing. These response properties may provide discrete signals that either modulate excitatory extensor drive during small load variations or inhibit support prior to compensatory stepping or initiation of swing.

Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations

The control of the legs of a walking hexapod is a complex problem as the legs have three joints each, resulting in a total of 18 degrees of freedom. We addressed this problem using a decentralized architecture termed Walknet, which consists of peripheral pattern generators being coordinated through influences acting mainly between neighbouring legs. Both, the coordinating influences and the local control modules (each acting only on one leg), are biologically inspired. This investigation shows that it is possible to adapt this approach to account for additional biological data by (1) changing the structure of the selector net in a biological plausible way (including force as an analog variable), (2) introducing a biologically motivated coordination influence for coactivation between legs and (3) adding a hypothetical influence between hind and front legs. This network of controllers has been tested using a dynamic simulation. It is able to describe (a) the behaviour of animals walking with one or two legs being amputated and (b) force oscillations that occur in a specific experimental situation, the standing legs of a walking animal.

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

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.

Control of swing movement: influences of differently shaped substrate

Stick insects were studied while walking on different substrates. The trajectories of swing movements are recorded. The starting position of a swing movement is varied in vertical direction and in the direction parallel to body long axis. The trajectories found cannot be predicted by an ANN (Swingnet1) proposed earlier to describe swing movements. However, a modified network (Swingnet2) allows for a satisfying description of the behavioral results. Walking on a narrow treadwheel leads to different swing trajectories compared to walking on a broad treadwheel. These trajectories cannot be described by Swingnet1, too. The form of the swing trajectory may depend on the direction of the force vector by which the leg acts on the ground in the preceding stance. Based on this assumption, an alternative hypothesis (Swingnet3) is proposed that can quantitatively describe all results of our experiment. When stick insects walk from a wide to a narrow substrate, transition between different swing trajectories does not change gradually over time. Rather, the form of the trajectory is determined by the current sensory input of the leg on a step-to-step basis. Finally, four different avoidance reflexes and their implementation into swing movements are investigated and described by a quantitative simulation.

Intersegmental transfer of sensory signals in the stick insect leg muscle control system

Intersegmental coordination during locomotion in legged animals arises from mechanical couplings and the exchange of neuronal information between legs. Here, the information flow from a single leg sense organ of the stick insect Cuniculina impigra onto motoneurons and interneurons of other legs was investigated. The femoral chordotonal organ (fCO) of the right middle leg, which measures posture and movement of the femur-tibia joint, was stimulated, and the responses of the tibial motoneuron pools of the other legs were recorded. In resting animals, fCO signals did not affect motoneuronal activity in neighboring legs. When the locomotor system was activated and antagonistic motoneurons were bursting in alternation, fCO stimuli facilitated transitions from flexor to extensor activity and vice versa in the contralateral leg. Following pharmacological treatment with picrotoxin, a blocker of GABA-ergic inhibition, the tibial motoneurons of all legs showed specific responses to signals from the middle leg fCO. For the contralateral middle leg we show that fCO signals encoding velocity and position of the tibia were processed by those identified local premotor nonspiking interneurons known to contribute to posture and movement control during standing and voluntary leg movements. Interneurons received both excitatory and inhibitory inputs, so that the response of some interneurons supported the motoneuronal output, while others opposed it. Our results demonstrate that sensory information from the fCO specifically affects the motoneuronal activity of other legs and that the layer of premotor nonspiking interneurons is a site of interaction between local proprioceptive sensory signals and proprioceptive signals from other legs.

Common motor mechanisms support body load in serially homologous legs of cockroaches in posture and walking

We studied the mechanisms underlying support of body load in posture and walking in serially homologous legs of cockroaches. Activities of the trochanteral extensor muscle in the front or middle legs were recorded neurographically while animals were videotaped. Body load was increased via magnets attached to the thorax and varied through a coil below the substrate. In posture, tonic firing of the slow trochanteral extensor motoneuron (Ds) in each leg was strongly modulated by changing body load. Rapid load increases produced decreases in body height and sharp increments in extensor firing. The peak of extensor activity more closely approximated the maximum velocity of body displacement than the body position. In walking, extensor bursts in front and middle legs were initiated during swing and continued into the stance phase. Moderate tonic increases in body load elicited similar, specific, phase dependent changes in both legs: extensor firing was not altered in swing but was higher after foot placement in stance. These motor adjustments to load are not anticipatory but apparently depend upon sensory feedback. These data are consistent with previous findings in the hind legs and support the idea that body load is countered by common motor mechanisms in serially homologous legs.

Dynamic simulation of insect walking

Insect walking relies on a complex interaction between the environment, body segments, muscles and the nervous system. For the stick insect in particular, previous investigations have highlighted the role of specific sensory signals in the timing of activity of central neural networks driving the individual leg joints. The objective of the current study was to relate specific sensory and neuronal mechanisms, known from experiments on reduced preparations, to the generation of the natural sequence of events forming the step cycle in a single leg. We have done this by simulating a dynamic 3D-biomechanical model of the stick insect coupled to a reduced model of the neural control system, incorporating only the mechanisms under study. The neural system sends muscle activation levels to the biomechanical system, which in turn provides correctly timed propriosensory signals back to the neural model. The first simulations were designed to test if the currently known mechanisms would be sufficient to explain the coordinated activation of the different leg muscles in the middle leg. Two experimental situations were mimicked: restricted stepping where only the coxa-trochanteral joint and the femur-tibia joint were free to move, and the unrestricted single leg movements on a friction-free surface. The first of these experimental situations is in fact similar to the preparation used in gathering much of the detailed knowledge on sensory and neuronal mechanisms. The simulations show that the mechanisms included can indeed account for the entire step cycle in both situations. The second aim was to test to what extent the same sensory and neuronal mechanisms would be adequate also for controlling the front and hind legs, despite the large differences in both leg morphology and kinematic patterns. The simulations show that front leg stepping can be generated by basically the same mechanisms while the hind leg control requires some reorganization. The simulations suggest that the influence from the femoral chordotonal organs on the network controlling levation-depression may have a reversed effect in the hind legs as compared to the middle and front legs. This, and other predictions from the model will have to be confirmed by additional experiments.

Behaviour-based modelling of hexapod locomotion: linking biology and technical application

Walking in insects and most six-legged robots requires simultaneous control of up to 18 joints. Moreover, the number of joints that are mechanically coupled via body and ground varies from one moment to the next, and external conditions such as friction, compliance and slope of the substrate are often unpredictable. Thus, walking behaviour requires adaptive, context-dependent control of many degrees of freedom. As a consequence, modelling legged locomotion addresses many aspects of any motor behaviour in general. Based on results from behavioural experiments on arthropods, we describe a kinematic model of hexapod walking: the distributed artificial neural network controller walknet. Conceptually, the model addresses three basic problems in legged locomotion. (I) First, coordination of several legs requires coupling between the step cycles of adjacent legs, optimising synergistic propulsion, but ensuring stability through flexible adjustment to external disturbances. A set of behaviourally derived leg coordination rules can account for decentralised generation of different gaits, and allows stable walking of the insect model as well as of a number of legged robots. (II) Second, a wide range of different leg movements must be possible, e.g. to search for foothold, grasp for objects or groom the body surface. We present a simple neural network controller that can simulate targeted swing trajectories, obstacle avoidance reflexes and cyclic searching-movements. (III) Third, control of mechanically coupled joints of the legs in stance is achieved by exploiting the physical interactions between body, legs and substrate. A local positive displacement feedback, acting on individual leg joints, transforms passive displacement of a joint into active movement, generating synergistic assistance reflexes in all mechanically coupled joints.

Interjoint coordination in the stick insect leg-control system: the role of positional signaling

Interjoint coordination is essential for proper walking behavior in multi-jointed insect legs. We have shown previously that movement signals from the femur-tibia (FT) joint can shape motor activity of the adjacent coxa-trochanter (CT) joint in the stick insect, Carausius morosus. Here, we present data on the role of position signals from the FT-joint on activity generated in motoneurons (MNs) of the CT-joint. We show that the probability of occurrence of stance (with depression in the CT-joint) or swing movements (with levation in the CT-joint) at the start of walking sequences is influenced by the angle of the FT-joint in the resting animal. We tested the influence of FT-joint angle on pharmacologically induced rhythmic activity of CT-joint depressor (DprTr) and levator (LevTr) MNs. The burst duration, mean spike rate within bursts, and duty cycle for each MN pool were found to depend on FT position. For LevTr MNs, these parameters progressively increased as the FT-joint was moved from extension to flexion, and the opposite was true for DprTr MNs. The cycle period of CT-MN rhythmicity also depended on FT position. In addition, we sometimes observed that the motor output shifted completely to one MN pool at extreme positions, suggesting that the central rhythm-generating network for the CT-joint became locked in one phase. These results indicate that position signals from the FT-joint modulate rhythmic activity in CT-joint MNs partly by having access to central rhythm generating networks of the CT-joint.

A biologically inspired controller for hexapod walking: simple solutions by exploiting physical properties

The locomotor system of slowly walking insects is well suited for coping with highly irregular terrain and therefore might represent a paragon for an artificial six-legged walking machine. Our investigations of the stick insect Carausius morosus indicate that these animals gain their adaptivity and flexibility mainly from the extremely decentralized organization of the control system that generates the leg movements. Neither the movement of a single leg nor the coordination of all six legs (i.e., the gait) appears to be centrally pre-programmed. Thus, instead of using a single, central controller with global knowledge, each leg appears to possess its own controller with only procedural knowledge for the generation of the leg's movement. This is possible because exploiting the physical properties avoids the need for complete information on the geometry of the system that would be a prerequisite for explicitly solving the problems. Hence, production of the gait is an emergent property of the whole system, in which each of the six single-leg controllers obeys a few simple and local rules in processing state-dependent information about its neighbors.

Load-regulating mechanisms in gait and posture: comparative aspects

How is load sensed by receptors, and how is this sensory information used to guide locomotion? Many insights in this domain have evolved from comparative studies since it has been realized that basic principles concerning load sensing and regulation can be found in a wide variety of animals, both vertebrate and invertebrate. Feedback about load is not only derived from specific load receptors but also from other types of receptors that previously were thought to have other functions. In the central nervous system of many species, a convergence is found between specific and nonspecific load receptors. Furthermore, feedback from load receptors onto central circuits involved in the generation of rhythmic locomotor output is commonly found. During the stance phase, afferent activity from various load detectors can activate the extensor part in such circuits, thereby providing reinforcing force feedback. At the same time, the flexion is suppressed. The functional role of this arrangement is that activity in antigravity muscles is promoted while the onset of the next flexion is delayed as long as the limb is loaded. This type of reinforcing force feedback is present during gait but absent in the immoble resting animal.

Cooperative mechanisms between leg joints of Carausius morosus I. Nonspiking interneurons that contribute to interjoint coordination

Three nonspiking interneurons are described in this paper that influence the activity of the motor neurons of three muscles of the proximal leg joints of the stick insect. Interneurons were recorded and stained intracellularly by glass microelectrodes; motor neurons were recorded extracellularly with oil-hook electrodes. The motor neurons innervate the two subcoxal muscles, the protractor and retractor coxae, and the thoracic part of the depressor trochanteris muscle. The latter spans the subcoxal joint before inserting the trochanter, thus coupling the two proximal joints mechanically. The three interneurons are briefly described here. First, interneuron NS 1 was known to become more excited during the swing phase of the rear and the stance phase of the middle leg. When depolarized it excited several motor neurons of the retractor coxae. This investigation revealed that it inhibits the activity of protractor and thoracic depressor motor neurons when depolarized as well. In a pilocarpine-activated animal, the membrane potential showed oscillations in phase with the activity of protractor motor neurons, suggesting that NS 1 might contribute to the transition from swing to stance movement. Second, interneuron NS 2 inhibits motor neurons of protractor and thoracic depressor when depolarized. In both a quiescent and a pilocarpine-activated animal, hyperpolarizing stimuli excite motor neurons of both muscles via disinhibition. In one active animal the disinhibiting stimuli were sufficient to generate swing-like movements of the leg. In pilocarpine-activated preparations the membrane potential oscillated in correlation with the motor neuronal activity of the protractor coxae and thoracic depressor muscle. Hyperpolarizing stimuli induced or reinforced the protractor and thoracic depressor bursts and inhibited the activity of the motor neurons of the retractor coxae muscle, the antagonistic muscle of the protractor. Therefore interneuron NS 2 can be regarded as an important premotor interneuron for the switching from stance to swing and from swing to stance. Finally, interneuron NS 3 inhibits the spontaneously active motor neurons of both motor neuron pools in the quiescent animal. During pilocarpine-induced protractor bursts, depolarizing stimuli applied to the interneuron excited several protractor motor neurons with large action potentials and one motor neuron of the thoracic depressor. No oscillations of the membrane potentials were observed. Therefore this interneuron might contribute to the generation of rapid leg movements. The results demonstrated that the two proximal joints are coupled not only mechanically but also neurally and that the thoracic part of the depressor appears to function as a part of the swing-generating system.

Cooperative mechanisms between leg joints of Carausius morosus II. Motor neuron activity and influence of conditional bursting interneuron

The activity of the motor neuron pools of the protractor coxae muscle and of the thoracic part of the depressor trochanteris muscle during forward walking in the stick insect was investigated, and a spiking local interneuron, able to produce "endogenous bursting" and innervating both motor neuron pools, was identified. Extracellular recordings of the motor neurons innervating the protractor and the thoracic depressor of front, middle, and rear legs, respectively, were made with oil-hook electrodes from the peripheral nerves nl2c and nl4a while the animals were walking on a styrofoam treadwheel. The corresponding leg movements were registered and phase histograms were created with the software Spike2. Intracellular recordings were made in the neuropile of the metathoracic ganglion with glass electrodes filled with the dye Lucifer yellow. In all three legs measured (front, middle, and rear), both motor neuron pools increased their activity during the swing movement. The increase in the activity of the protractor motor neurons started at the end of the stance approximately 100 ms before reaching the posterior extreme position (PEP), and the activity of the large-sized depressor motor neurons increased as soon as the tarsus was lifted at the PEP. A local spiking interneuron was identified that excited both motor neuron pools. In 4 of 23 recordings the interneuron started to burst in synchrony with protractor and thoracic depressor motor neurons. During bursting a depolarizing stimulus reinforced and a hyperpolarizing stimulus inhibited the activity of both motor neuron pools. Thus we conclude that the thoracic part of the depressor trochanteris muscle might be a component of the neuromuscular system that shapes the swing movement. The two proximal joints, subcoxal and coxa-trochanter, connected mechanically via the thoracic part of the depressor trochanteris muscle, are also connected neurally by segmental and intersegmental spiking interneurons (this paper) and by nonspiking local interneurons (see companion paper).