Slow temporal filtering may largely explain the transformation of stick insect (Carausius morosus) extensor motor neuron activity into muscle movement

Locomotor resilience through load-dependent modulation of muscle co-contraction

Terrestrial locomotor behavior in variable environments requires resilience to sudden changes in substrate properties. For example, walking animals can adjust to substantial changes in slope and corresponding changes in load distribution among legs. In insects, slope-dependent adjustments have mainly been examined under steady-state conditions, whereas the transition dynamics have been largely neglected. In a previous study, we showed that steady-state adjustments of stick insects to ±45° slopes involve substantial changes in joint torques and muscle activity with only minor changes in leg kinematics. Here, we take a close look at the time course of these adjustments as stick insects compensate for various kinds of disturbances to load distribution. In particular, we test whether the transition from one steady state to another involves distinct transition steps or follows a graded process. To resolve this, we combined simultaneous recordings of whole-body kinematics and hind leg muscle activity to elucidate how freely walking Carausius morosus negotiated a step-change in substrate slope. Step-by-step adjustments reveal that muscle activity changed in a graded manner as a function of body pitch relative to gravity. We further show analogous transient adjustment of muscle activity in response to destabilizing lift-off events of neighboring legs and the disappearance of antagonist co-activation during crawling episodes. Given these three examples of load-dependent regulation of antagonist muscle co-contraction, we conclude that stick insects respond to both transient and sustained changes in load distribution by regulating joint stiffness rather than through distinct transition steps.

Non-linear multimodal integration in a distributed premotor network controls proprioceptive reflex gain in the insect leg

Producing context-appropriate motor acts requires integrating multiple sensory modalities. Presynaptic inhibition of proprioceptive afferent neurons^1-4 and afferents of different modalities targeting the same motor neurons (MNs)^5-7 underlies some of this integration. However, in most systems, an interneuronal network is interposed between sensory afferents and MNs. How these networks contribute to this integration, particularly at single-neuron resolution, is little understood. Context-specific integration of load and movement sensory inputs occurs in the stick insect locomotory system,^6,8-12 and both inputs feed into a network of premotor nonspiking interneurons (NSIs).⁸ We analyzed how load altered movement signal processing in the stick insect femur-tibia (FTi) joint control system by tracing the interaction of FTi movement^13-15 (femoral chordotonal organ [fCO]) and load^13,15,16 (tibial campaniform sensilla [CS]) signals through the NSI network to the slow extensor tibiae (SETi) MN, the extensor MN primarily active in non-walking animals.^17-19 On the afferent level, load reduced movement signal gain by presynaptic inhibition. In the NSI network, graded responses to movement and load inputs summed nonlinearly, increasing the gain of NSIs opposing movement-induced reflexes and thus decreasing the SETi and extensor tibiae muscle movement reflex responses. Gain modulation was movement-parameter specific and required presynaptic inhibition. These data suggest that gain changes in distributed premotor networks, specifically the relative weighting of antagonistic pathways, could be a general mechanism by which multiple sensory modalities are integrated to generate context-appropriate motor activity.

Temporal differences between load and movement signal integration in the sensorimotor network of an insect leg

Nervous systems face a torrent of sensory inputs, including proprioceptive feedback. Signal integration depends on spatially and temporally coinciding signals. It is unclear how relative time delays affect multimodal signal integration from spatially distant sense organs. We measured transmission times and latencies along all processing stages of sensorimotor pathways in the stick insect leg muscle control system, using intra- and extracellular recordings. Transmission times of signals from load-sensing tibial and trochanterofemoral campaniform sensilla (tiCS, tr/fCS) to the premotor network were longer than from the movement-sensing femoral chordotonal organ (fCO). We characterized connectivity patterns from tiCS, tr/fCS, and fCO afferents to identified premotor nonspiking interneurons (NSIs) and motor neurons (MNs) by distinguishing short- and long-latency responses to sensory stimuli. Functional NSI connectivity depended on sensory context. The timeline of multisensory integration in the NSI network showed an early phase of movement signal processing and a delayed phase of load signal integration. The temporal delay of load signals relative to movement feedback persisted into MN activity and muscle force development. We demonstrate differential delays in the processing of two distinct sensory modalities generated by the sensorimotor network and affecting motor output. The reported temporal differences in sensory processing and signal integration improve our understanding of sensory network computation and function in motor control.NEW & NOTEWORTHY Networks integrating multisensory input face the challenge of not only spatial but also temporal integration. In the local network controlling insect leg movements, proprioceptive signal delays differ between sensory modalities. Specifically, signal transmission times to and neuronal connectivity within the sensorimotor network lead to delayed information about leg loading relative to movement signals. Temporal delays persist up to the level of the motor output, demonstrating its relevance for motor control.

Neuromodulation Can Be Simple: Myoinhibitory Peptide, Contained in Dedicated Regulatory Pathways, Is the Only Neurally-Mediated Peptide Modulator of Stick Insect Leg Muscle

In the best studied cases (Aplysia feeding, crustacean stomatogastric system), peptidergic modulation is mediated by large numbers of peptides. Furthermore, in Aplysia, excitatory motor neurons release the peptides, obligatorily coupling target activation and modulator release. Vertebrate nervous systems typically contain about a hundred peptide modulators. These data have created a belief that modulation is, in general, complex. The stick insect leg is a well-studied locomotory model system, and the complete stick insect neuropeptide inventory was recently described. We used multiple techniques to comprehensively examine stick insect leg peptidergic modulation. Single-cell mass spectrometry (MS) and immunohistochemistry showed that myoinhibitory peptide (MIP) is the only neuronal (as opposed to hemolymph-borne) peptide modulator of all leg muscles. Leg muscle excitatory motor neurons contained no neuropeptides. Only the common inhibitor (CI) and dorsal unpaired median (DUM) neuron groups, each neuron of which innervates a group of functionally-related leg muscles, contained MIP. We described MIP transport to, and receptor presence in, one leg muscle, the extensor tibiae (ExtTi). MIP application reduced ExtTi slow fiber force and shortening by about half, increasing the muscle's ability to contract and relax rapidly. These data show neuromodulation does not need to be complex. Excitation and modulation do not need to be obligatorily coupled (Aplysia feeding). Modulation does not need to involve large numbers of peptides, with the attendant possibility of combinatorial explosion (stomatogastric system). Modulation can be simple, mediated by dedicated regulatory neurons, each innervating a single group of functionally-related targets, and all using the same neuropeptide.SIGNIFICANCE STATEMENT Vertebrate and invertebrate nervous systems contain large numbers (around a hundred in human brain) of peptide neurotransmitters. In prior work, neuropeptide modulation has been complex, either obligatorily coupling postsynaptic excitation and modulation, or large numbers of peptides modulating individual neural networks. The complete stick insect neuropeptide inventory was recently described. We comprehensively describe here peptidergic modulation in the stick insect leg. Surprisingly, out of the large number of potential peptide transmitters, only myoinhibitory peptide (MIP) was present in neurons innervating leg muscles. Furthermore, the peptide was present only in dedicated regulatory neurons, not in leg excitatory motor neurons. Peptidergic modulation can thus be simple, neither obligatorily coupling target activation and modulation nor involving so many peptides that combinatorial explosion can occur.

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.

Evaluation of linear and non-linear activation dynamics models for insect muscle

In computational modelling of sensory-motor control, the dynamics of muscle contraction is an important determinant of movement timing and joint stiffness. This is particularly so in animals with many slow muscles, as is the case in insects-many of which are important models for sensory-motor control. A muscle model is generally used to transform motoneuronal input into muscle force. Although standard models exist for vertebrate muscle innervated by many motoneurons, there is no agreement on a parametric model for single motoneuron stimulation of invertebrate muscle. Although several different models have been proposed, they have never been evaluated using a common experimental data set. We evaluate five models for isometric force production of a well-studied model system: the locust hind leg tibial extensor muscle. The response of this muscle to motoneuron spikes is best modelled as a non-linear low-pass system. Linear first-order models can approximate isometric force time courses well at high spike rates, but they cannot account for appropriate force time courses at low spike rates. A linear third-order model performs better, but only non-linear models can account for frequency-dependent change of decay time and force potentiation at intermediate stimulus frequencies. Some of the differences among published models are due to differences among experimental data sets. We developed a comprehensive toolbox for modelling muscle activation dynamics, and optimised model parameters using one data set. The "Hatze-Zakotnik model" that emphasizes an accurate single-twitch time course and uses frequency-dependent modulation of the twitch for force potentiation performs best for the slow motoneuron. Frequency-dependent modulation of a single twitch works less well for the fast motoneuron. The non-linear "Wilson" model that optimises parameters to all data set parts simultaneously performs better here. Our open-access toolbox provides powerful tools for researchers to fit appropriate models to a range of insect muscles.

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.

Swing Velocity Profiles of Small Limbs Can Arise from Transient Passive Torques of the Antagonist Muscle Alone

In large limbs, changing motor neuron activity typically controls within-movement velocity. For example, sequential agonist-antagonist-agonist motor neuron firing typically underlies the slowing often present at the end of human reaches. In physiological movements of large limbs, antagonistic muscle passive torque is generally negligible. In small limbs, alternatively, passive torques can determine limb rest position, generate restoring movements to it, and decrease agonist-generated movement amplitude and velocity maxima. These observations suggest that, in small limbs, passive forces might also control velocity changes within movements. We investigated this issue in stick insect middle leg femur-tibia (FT) joint. During swing, the FT joint extensor muscle actively shortens and the flexor muscle passively lengthens. As in human reaching, after its initial acceleration, FT joint velocity continuously decreases. We measured flexor passive forces during imposed stretches spanning the ranges of FT joint angles, angular velocities, and movement amplitudes present in leg swings. The viscoelastic "transient" passive force that occurs during and soon after stretch depended on all three variables and could be tens of times larger than the "steady-state" passive force commonly measured long after stretch end. We combined these data, the flexor and extensor moment arms, and an existing extensor model to simulate FT joint swing. To measure only passive (flexor) muscle-dependent effects, we used constant extensor activations in these simulations. In simulations using data from ten flexor muscles, flexor passive torque could always produce swings with, after swing initiation, continuously decreasing velocities. Antagonist muscle passive torques alone can thus control within-movement velocity.

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.

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.

Cell-selective modulation of the Drosophila neuromuscular system by a neuropeptide

Neuropeptides can modulate physiological properties of neurons in a cell-specific manner. The present work examines whether a neuropeptide can also modulate muscle tissue in a cell-specific manner using identified muscle cells in third-instar larvae of fruit flies. DPKQDFMRFa, a modulatory peptide in the fruit fly Drosophila melanogaster, has been shown to enhance transmitter release from motor neurons and to elicit contractions by a direct effect on muscle cells. We report that DPKQDFMRFa causes a nifedipine-sensitive drop in input resistance in some muscle cells (6 and 7) but not others (12 and 13). The peptide also increased the amplitude of nerve-evoked contractions and compound excitatory junctional potentials (EJPs) to a greater degree in muscle cells 6 and 7 than 12 and 13. Knocking down FMRFamide receptor (FR) expression separately in nerve and muscle indicate that both presynaptic and postsynaptic FR expression contributed to the enhanced contractions, but EJP enhancement was mainly due to presynaptic expression. Muscle ablation showed that DPKQDFMRFa induced contractions and enhanced nerve-evoked contractions more strongly in muscle cells 6 and 7 than cells 12 and 13. In situ hybridization indicated that FR expression was significantly greater in muscle cells 6 and 7 than 12 and 13. Taken together, these results indicate that DPKQDFMRFa can elicit cell-selective effects on muscle fibers. The ability of neuropeptides to work in a cell-selective manner on neurons and muscle cells may help explain why so many peptides are encoded in invertebrate and vertebrate genomes.

Determining all parameters necessary to build Hill-type muscle models from experiments on single muscles

Characterizing muscle requires measuring such properties as force-length, force-activation, and force-velocity curves. These characterizations require large numbers of data points because both what type of function (e.g., linear, exponential, hyperbolic) best represents each property, and the values of the parameters in the relevant equations, need to be determined. Only a few properties are therefore generally measured in experiments on any one muscle, and complete characterizations are obtained by averaging data across a large number of muscles. Such averaging approaches can work well for muscles that are similar across individuals. However, considerable evidence indicates that large inter-individual variation exists, at least for some muscles. This variation poses difficulties for across-animal averaging approaches. Methods to fully describe all muscle's characteristics in experiments on individual muscles would therefore be useful. Prior work in stick insect extensor muscle has identified what functions describe each of this muscle's properties and shown that these equations apply across animals. Characterizing these muscles on an individual-by-individual basis therefore requires determining only the values of the parameters in these equations, not equation form. We present here techniques that allow determining all these parameter values in experiments on single muscles. This technique will allow us to compare parameter variation across individuals and to model muscles individually. Similar experiments can likely be performed on single muscles in other systems. This approach may thus provide a widely applicable method for characterizing and modeling muscles from single experiments.

Using individual-muscle specific instead of across-muscle mean data halves muscle simulation error

Hill-type parameter values measured in experiments on single muscles show large across-muscle variation. Using individual-muscle specific values instead of the more standard approach of across-muscle means might therefore improve muscle model performance. We show here that using mean values increased simulation normalized RMS error in all tested motor nerve stimulation paradigms in both isotonic and isometric conditions, doubling mean simulation error from 9 to 18 (different at p < 0.0001). These data suggest muscle-specific measurement of Hill-type model parameters is necessary in work requiring highly accurate muscle model construction. Maximum muscle force (F (max)) showed large (fourfold) across-muscle variation. To test the role of F (max) in model performance we compared the errors of models using mean F (max) and muscle-specific values for the other model parameters, and models using muscle-specific F (max) values and mean values for the other model parameters. Using muscle-specific F (max) values did not improve model performance compared to using mean values for all parameters, but using muscle-specific values for all parameters but F (max) did (to an error of 14, different from muscle-specific, mean all parameters, and mean only F (max) errors at p ≤ 0.014). Significantly improving model performance thus required muscle-specific values for at least a subset of parameters other than F (max), and best performance required muscle-specific values for this subset and F (max). Detailed consideration of model performance suggested that remaining model error likely stemmed from activation of both fast and slow motor neurons in our experiments and inadequate specification of model activation dynamics.

Slow motor neuron stimulation of locust skeletal muscle: model and measurement

The isometric force response of the locust hind leg extensor tibia muscle to stimulation of a slow extensor tibia motor neuron is experimentally investigated, and a mathematical model describing the response presented. The measured force response was modelled by considering the ability of an existing model, developed to describe the response to the stimulation of a fast extensor tibia motor neuron and to also model the response to slow motor neuron stimulation. It is found that despite large differences in the force response to slow and fast motor neuron stimulation, which could be accounted for by the differing physiology of the fibres they innervate, the model is able to describe the response to both fast and slow motor neuron stimulation. Thus, the presented model provides a potentially generally applicable, robust, simple model to describe the isometric force response of a range of muscles.

Relating neuronal to behavioral performance: variability of optomotor responses in the blowfly

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system.

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.

Control of stepping velocity in the stick insect Carausius morosus

We performed electrophysiological and behavioral experiments in single-leg preparations and intact animals of the stick insect Carausius morosus to understand mechanisms underlying the control of walking speed. At the level of the single leg, we found no significant correlation between stepping velocity and spike frequency of motor neurons (MNs) other than the previously shown modification in flexor (stance) MN activity. However, pauses between stance and swing motoneuron activity at the transition from stance to swing phase and stepping velocity are correlated. Pauses become shorter with increasing speed and completely disappear during fast stepping sequences. By means of extra- and intracellular recordings in single-leg stick insect preparations we found no systematic relationship between the velocity of a stepping front leg and the motoneuronal activity in the ipsi- or contralateral mesothoracic protractor and retractor, as well as flexor and extensor MNs. The observations on the lack of coordination of stepping velocity between legs in single-leg preparations were confirmed in behavioral experiments with intact stick insects tethered above a slippery surface, thereby effectively removing mechanical coupling through the ground. In this situation, there were again no systematic correlations between the stepping velocities of different legs, despite the finding that an increase in stepping velocity in a single front leg is correlated with a general increase in nerve activity in all connectives between the subesophageal and all thoracic ganglia. However, when the tethered animal increased walking speed due to a short tactile stimulus, provoking an escape-like response, stepping velocities of ipsilateral legs were found to be correlated for several steps. These results indicate that there is no permanent coordination of stepping velocities between legs, but that such coordination can be activated under certain circumstances.

Neural control of unloaded leg posture and of leg swing in stick insect, cockroach, and mouse differs from that in larger animals

Stick insect (Carausius morosus) leg muscles contract and relax slowly. Control of stick insect leg posture and movement could therefore differ from that in animals with faster muscles. Consistent with this possibility, stick insect legs maintained constant posture without leg motor nerve activity when the animals were rotated in air. That unloaded leg posture was an intrinsic property of the legs was confirmed by showing that isolated legs had constant, gravity-independent postures. Muscle ablation experiments, experiments showing that leg muscle passive forces were large compared with gravitational forces, and experiments showing that, at the rest postures, agonist and antagonist muscles generated equal forces indicated that these postures depended in part on leg muscles. Leg muscle recordings showed that stick insect swing motor neurons fired throughout the entirety of swing. To test whether these results were specific to stick insect, we repeated some of these experiments in cockroach (Periplaneta americana) and mouse. Isolated cockroach legs also had gravity-independent rest positions and mouse swing motor neurons also fired throughout the entirety of swing. These data differ from those in human and horse but not cat. These size-dependent variations in whether legs have constant, gravity-independent postures, in whether swing motor neurons fire throughout the entirety of swing, and calculations of how quickly passive muscle force would slow limb movement as limb size varies suggest that these differences may be caused by scaling. Limb size may thus be as great a determinant as phylogenetic position of unloaded limb motor control strategy.