Tight turns in stick insects

Thorax-Segment- and Leg-Segment-Specific Motor Control for Adaptive Behavior

We have just started to understand the mechanisms underlying flexibility of motor programs among segmental neural networks that control each individual leg during walking in vertebrates and invertebrates. Here, we investigated the mechanisms underlying curve walking in the stick insect Carausius morosus during optomotor-induced turning. We wanted to know, whether the previously reported body-side specific changes in a two-front leg turning animal are also observed in the other thoracic leg segments. The motor activity of the three major leg joints showed three types of responses: 1) a context-dependent increase or decrease in motor neuron (MN) activity of the antagonistic MN pools of the thorax-coxa (ThC)-joint during inside and outside turns; 2) an activation of 1 MN pool with simultaneous cessation of the other, independent of the turning direction in the coxa-trochanteral (CTr)-joint; 3) a modification in the activity of both FTi-joint MN pools which depended on the turning direction in one, but not in the other thorax segment. By pharmacological activation of the meso- or metathoracic central pattern generating networks (CPG), we show that turning-related modifications in motor output involve changes to local CPG activity. The rhythmic activity in the MN pools of the ThC and CTr-joints was modified similarly to what was observed under control conditions in saline. Our results indicate that changes in meso- and metathoracic motor activity during curve walking are leg-joint- and thorax-segment-specific, can depend on the turning direction, and are mediated through changes in local CPG activity.

Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results

Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that (sensory-modulated) central pattern generators (CPG) are required to control the rhythmic movements of walking legs. Here, we investigate a different view. To this end, we introduce a sensor based controller operating on artificial neurons, being applied to a (simulated) insectoid robot required to exploit the "loop through the world" allowing for simplification of neural computation. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a broad range of behaviors never dealt with in a single system by earlier approaches. This includes the ability to produce footfall patterns such as velocity dependent "tripod", "tetrapod", "pentapod" as well as various stable intermediate patterns as observed in stick insects and in Drosophila. These patterns are found to be stable against disturbances and when starting from various leg configurations. Our neuronal architecture easily allows for starting or interrupting a walk, all being difficult for CPG controlled solutions. Furthermore, negotiation of curves and walking on a treadmill with various treatments of individual legs is possible as well as backward walking and performing short steps. This approach can as well account for the neurophysiological results usually interpreted to support the idea that CPGs form the basis of walking, although our approach is not relying on explicit CPG-like structures. Application of CPGs may however be required for very fast walking. Our neuronal structure allows to pinpoint specific neurons known from various insect studies. Interestingly, specific common properties observed in both insects and crustaceans suggest a significance of our controller beyond the realm of insects.

Body side-specific changes in sensorimotor processing of movement feedback in a walking insect

Feedback from load and movement sensors can modify timing and magnitude of the motor output in the stepping stick insect. One source of feedback is stretch reception by the femoral chordotonal organ (fCO), which encodes such parameters as the femorotibial (FTi) joint angle, the angular velocity, and its acceleration. Stimulation of the fCO causes a postural resistance reflex, during quiescence, and can elicit the opposite, so-called active reaction (AR), which assists ongoing flexion during active movements. In the present study, we investigated the role of fCO feedback for the difference in likelihood of generating ARs on the inside vs. the outside during curve stepping. We analyzed the effects of fCO stimulation on the motor output to the FTi and the neighboring coxa-trochanter and thorax-coxa joints of the middle leg. In inside and outside turns, the probability for ARs increases with increasing starting angle and decreasing stimulus velocity; furthermore, it is independent of the total angular excursion. However, the transition between stance and swing motor activity always occurs after a specific angular excursion, independent of the turning direction. Feedback from the fCO also has an excitatory influence on levator trochanteris motoneurons (MNs) during inside and outside turns, whereas the same feedback affects protractor coxae MNs only during outside steps. Our results suggest joint- and body side-dependent processing of fCO feedback. A shift in gain may be responsible for different AR probabilities between inside and outside turning, whereas the general control mechanism for ARs is unchanged.NEW & NOTEWORTHY We show that parameters of movement feedback from the tibia in an insect during curve walking are processed in a body side-specific manner, and how. From our results it is highly conceivable that the difference in motor response to the feedback supports the body side-specific leg kinematics during turning. Future studies will need to determine the source for the inputs that determine the local changes in sensory-motor processing.

The manifold structure of limb coordination in walking Drosophila

Terrestrial locomotion requires animals to coordinate their limb movements to efficiently traverse their environment. While previous studies in hexapods have reported that limb coordination patterns can vary substantially, the structure of this variability is not yet well understood. Here, we characterized the symmetric and asymmetric components of variation in walking kinematics in the genetic model organism Drosophila. We found that Drosophila use a single continuum of coordination patterns without evidence for preferred configurations. Spontaneous symmetric variability was associated with modulation of a single control parameter-stance duration-while asymmetric variability consisted of small, limb-specific modulations along multiple dimensions of the underlying symmetric pattern. Commands that modulated walking speed, originating from artificial neural activation or from the visual system, evoked modulations consistent with spontaneous behavior. Our findings suggest that Drosophila employ a low-dimensional control architecture, which provides a framework for understanding the neural circuits that regulate hexapod legged locomotion.

Transfer of Spatial Contact Information Among Limbs and the Notion of Peripersonal Space in Insects

Internal representation of far-range space in insects is well established, as it is necessary for navigation behavior. Although it is likely that insects also have an internal representation of near-range space, the behavioral evidence for the latter is much less evident. Here, we estimate the size and shape of the spatial equivalent of a near-range representation that is constituted by somatosensory sampling events. To do so, we use a large set of experimental whole-body motion capture data on unrestrained walking, climbing and searching behavior in stick insects of the species Carausius morosus to delineate ‘action volumes’ and ‘contact volumes’ for both antennae and all six legs. As these volumes are derived from recorded sampling events, they comprise a volume equivalent to a representation of coinciding somatosensory and motor activity. Accordingly, we define this volume as the peripersonal space of an insect. It is of immediate behavioral relevance, because it comprises all potential external object locations within the action range of the body. In a next step, we introduce the notion of an affordance space as that part of peripersonal space within which contact-induced spatial estimates lie within the action ranges of more than one limb. Because the action volumes of limbs overlap in this affordance space, spatial information from one limb can be used to control the movement of another limb. Thus, it gives rise to an affordance as known for contact-induced reaching movements and spatial coordination of footfall patterns in stick insects. Finally, we probe the computational properties of the experimentally derived affordance space for pairs of neighboring legs. This is done by use of artificial neural networks that map the posture of one leg into a target posture of another leg with identical foot position.

Body side-specific control of motor activity during turning in a walking animal

Animals and humans need to move deftly and flexibly to adapt to environmental demands. Despite a large body of work on the neural control of walking in invertebrates and vertebrates alike, the mechanisms underlying the motor flexibility that is needed to adjust the motor behavior remain largely unknown. Here, we investigated optomotor-induced turning and the neuronal mechanisms underlying the differences between the leg movements of the two body sides in the stick insect Carausius morosus. We present data to show that the generation of turning kinematics in an insect are the combined result of descending unilateral commands that change the leg motor output via task-specific modifications in the processing of local sensory feedback as well as modification of the activity of local central pattern generating networks in a body-side-specific way. To our knowledge, this is the first study to demonstrate the specificity of such modifications in a defined motor task.

DOI:http://dx.doi.org/10.7554/eLife.13799.001

Walking along a curve or turning is a complex manoeuvre for the nervous system, as it must coordinate different leg movements on each side of the body. Rhythmic processes such as walking are controlled by networks of neurons called central pattern generators. The resulting movements can be adjusted by feedback from sense organs in response to environmental conditions. For example, sensory feedback that provides information about the load placed on each leg, allows the animal to control the duration of a stance. How the nerve cells, or neurons, involved in these processes work together to produce complex, flexible movements such as turning is largely unknown.

Previous work on how the brain negotiates turning movements has been carried out mostly in animals that swim or fly. To understand what happens during walking, Gruhn et al. monitored stick insects that walked in a curve on a slippery surface, and recorded the electrical activity within the animals' nervous system as they turned.

By comparing the activity of the nervous system on each side of the body while the insects walked a curve, Gruhn et al. found that the nervous system uses at least three different mechanisms to produce the different movements on the inside and outside. Firstly, the sensory feedback signals that communicate the load on the leg are processed in the legs on the outside of the curve to support forward steps, while they are processed on the inside legs to support forward, sideward, and backward steps. Secondly, the motor activity produced by the central pattern generator is modulated to be stronger for the muscle that moves the leg backward on the outside of the curve. At the same time, this activity is stronger for the muscle that moves the leg forward on the inside of the curve. Thirdly, signals from a front leg influence the movement of the other legs on the same side of the body. This influence is strong on the inside and weak on the outside of the curve.

Together or separately, these three mechanisms could provide the animal with the means to perform turns in all their different curvatures. Future work will need to work out exactly which local neurons process the signals sent from the brain to control movement.

DOI:http://dx.doi.org/10.7554/eLife.13799.002

Exceptional running and turning performance in a mite

The Southern California endemic mite, Paratarsotomus macropalpis, was filmed in the field on a concrete substrate and in the lab to analyze stride frequency, gait, and running speed under different temperature conditions and during turning. At ground temperatures ranging from 45°C to 60°C, mites ran at a mean relative speed of 192.4±2.1 BL s−1 (body lengths per second), exceeding the highest previously documented value for a land animal by 12.5%. Stride frequencies were also exceptionally high (up to 135 Hz), and increased with substrate temperature. Juveniles exhibited higher relative speeds than adults and possess proportionally longer legs, which allow for greater relative stride lengths. Although mites accelerated and decelerated rapidly during straight running (7.2±1.2 ms−2 and −10.1±2.1 ms−2, respectively), the forces involved were comparable to those found in other animals. P. macropalpis employs an alternating tetrapod gait during steady running. Shallow turns were accomplished by a simple asymmetry in stride length. During tight turns, mites pivoted around the tarsus of the inside third leg (L3), which thus behaved like a grappling hook. Pivot turns were characterized by a 42% decrease in turning radius and 40% increase in angular velocity compared to non-pivot turns. The joint angle amplitudes of the inner L2 and L3 were negligible during a pivot turn. While exceptional, running speeds in P. macropalpis approximate values predicted from inter-specific scaling relationships.

A hexapod walker using a heterarchical architecture for action selection

Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor) states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network (RNN) of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module.

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.

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

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.

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.