Premotor Interneurons in the Local Control of Stepping Motor Output for the Stick Insect Single Middle Leg

Neurons with names: Descending control and sensorimotor processing in insect motor control

Technical and methodological advances in recent years have brought new ways to tackle major classical questions in insect motor control. Particularly, significant advancements were achieved in comprehending brain descending control by characterizing descending neurons, their targets in the ventral nerve cord (VNC), and how local networks there integrate sensory information. While physiological experiments in larger insects brought us a better understanding of how sensory modalities are processed locally in the VNC, the development and improvement of genetic tools, principally in Drosophila, opened the door to individually characterize actors at these three levels of information flow in behavioral control. This brief review brings together the names and roles of some of those actors, by highlighting the most significant findings from our perspective.

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.

Distributed processing of load and movement feedback in the premotor network controlling an insect leg joint

In legged animals, integration of information from various proprioceptors in and on the appendages by local premotor networks in the central nervous system is crucial for controlling motor output. To ensure posture maintenance and precise active movements, information about limb loading and movement is required. In insects, various groups of campaniform sensilla (CS) measure forces and loads acting in different directions on the leg, and the femoral chordotonal organ (fCO) provides information about movement of the femur-tibia (FTi) joint. In this study, we used extra- and intracellular recordings of extensor tibiae (ExtTi) and retractor coxae (RetCx) motor neurons (MNs) and identified local premotor nonspiking interneurons (NSIs) and mechanical stimulation of the fCO and tibial or trochanterofemoral CS (tiCS, tr/fCS), to investigate the premotor network architecture underlying multimodal proprioceptive integration. We found that load feedback from tiCS altered the strength of movement-elicited resistance reflexes and determined the specificity of ExtTi and RetCx MN responses to various load and movement stimuli. These responses were mediated by a common population of identified NSIs into which synaptic inputs from the fCO, tiCS, and tr/fCS are distributed, and whose effects onto ExtTi MNs can be antagonistic for both stimulus modalities. Multimodal sensory signal interaction was found at the level of single NSIs and MNs. The results provide evidence that load and movement feedback are integrated in a multimodal, distributed local premotor network consisting of antagonistic elements controlling movements of the FTi joint, thus substantially extending current knowledge on how legged motor systems achieve fine-tuned motor control.NEW & NOTEWORTHY Proprioception is crucial for motor control in legged animals. We show the extent to which processing of movement (fCO) and load (CS) signals overlaps in the local premotor network of an insect leg. Multimodal signals converge onto the same set of interneurons, and our knowledge about distributed, antagonistic processing is extended to incorporate multiple modalities within one perceptual neuronal framework.

Integrating Non-spiking Interneurons in Spiking Neural Networks

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

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.

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.

Modeling search movements of an insect's front leg

Beside locomotion, search movements are another important type of motor activity of insects. They are very often performed by the front legs of the animals. They consist of cyclic stereotypical leg movements that can be modified by sensory signals. The details of the local organization of these movements have however not yet been studied. In this paper, we, using an appropriate variant of our existing one-leg model, present a scheme of how these searching movements might be organized and performed on the level of local neuromuscular control networks. In the simulations with the model, we attempted to mimic the experimental results by Berg et al. (J. Exp. Biol. 216:1064-1074, 2013) in which an obstacle was put in the way of the search movements of the front leg for a very short while, and then the recovery to the usual search movements was observed and analyzed. Our simulation results suggest that the recruitment of the fast levator and depressor muscles play a crucial part in resuming the search movements after removal of the obstacle. The interplay between the levator and depressor, and the extensor and flexor local control networks can, according to the model, bring about a large variety of search movements upon removal of the obstacle. A number of these movements are comparable with those seen in the experiments.

Six-legged walking in insects: how CPGs, peripheral feedback, and descending signals generate coordinated and adaptive motor rhythms

Walking is a rhythmic locomotor behavior of legged animals, and its underlying mechanisms have been the subject of neurobiological research for more than 100 years. In this article, we review relevant historical aspects and contemporary studies in this field of research with a particular focus on the role of central pattern generating networks (CPGs) and their contribution to the generation of six-legged walking in insects. Aspects of importance are the generation of single-leg stepping, the generation of interleg coordination, and how descending signals influence walking. We first review how CPGs interact with sensory signals from the leg in the generation of leg stepping. Next, we summarize how these interactions are modified in the generation of motor flexibility for forward and backward walking, curve walking, and speed changes. We then review the present state of knowledge with regard to the role of CPGs in intersegmental coordination and how CPGs might be involved in mediating descending influences from the brain for the initiation, maintenance, modification, and cessation of the motor output for walking. Throughout, we aim to specifically address gaps in knowledge, and we describe potential future avenues and approaches, conceptual and methodological, with the latter emphasizing in particular options arising from the advent of neurogenetic approaches to this field of research and its combination with traditional approaches.

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.

A leg-local neural mechanism mediates the decision to search in stick insects

In many animals, individual legs can either function independently, as in behaviors such as scratching or searching, or be used in coordinated patterns with other legs, as in walking or climbing. While the control of walking has been extensively investigated, the mechanisms mediating the behavioral choice to activate individual legs independently are poorly understood. We examined this issue in stick insects, in which each leg can independently produce a rhythmic searching motor pattern if it doesn't find a foothold [1-4]. We show here that one non-spiking interneuron, I4, controls searching behavior in individual legs. One I4 is present in each hemi-segment of the three thoracic ganglia [5, 6]. Search-inducing sensory input depolarizes I4. I4 activity was necessary and sufficient to initiate and maintain searching movements. When substrate contact was provided, I4 depolarization no longer induced searching. I4 therefore both integrates search-inducing sensory input and is gated out by other sensory input (substrate contact). Searching thus occurs only when it is behaviorally appropriate. I4 depolarization never elicited stepping. These data show that individual, locally activated neurons can mediate the behavioral choice to use individual legs independently. This mechanism may be particularly important in insects' front legs, which can function independently like vertebrate arms and hands [7]. Similar local command mechanisms that selectively activate the pattern generators controlling repeated functional units such as legs or body segments may be present in other systems.

Controlling legs for locomotion-insights from robotics and neurobiology

Walking is the most common terrestrial form of locomotion in animals. Its great versatility and flexibility has led to many attempts at building walking machines with similar capabilities. The control of walking is an active research area both in neurobiology and robotics, with a large and growing body of work. This paper gives an overview of the current knowledge on the control of legged locomotion in animals and machines and attempts to give walking control researchers from biology and robotics an overview of the current knowledge in both fields. We try to summarize the knowledge on the neurobiological basis of walking control in animals, emphasizing common principles seen in different species. In a section on walking robots, we review common approaches to walking controller design with a slight emphasis on biped walking control. We show where parallels between robotic and neurobiological walking controllers exist and how robotics and biology may benefit from each other. Finally, we discuss where research in the two fields diverges and suggest ways to bridge these gaps.

Task-dependent modification of leg motor neuron synaptic input underlying changes in walking direction and walking speed

Animals modify their behavior constantly to perform adequately in their environment. In terrestrial locomotion many forms of adaptation exist. Two tasks are changes of walking direction and walking speed. We investigated these two changes in motor output in the stick insect Cuniculina impigra to see how they are brought about at the level of leg motor neurons. We used a semi-intact preparation in which we can record intracellularly from leg motor neurons during walking. In this single-leg preparation the middle leg of the animal steps in a vertical plane on a treadwheel. Stimulation of either abdomen or head reliably elicits fictive forward or backward motor activity, respectively, in the fixed and otherwise deafferented thorax-coxa joint. With a change of walking direction only thorax-coxa-joint motor neurons protractor and retractor changed their activity. The protractor switched from swing activity during forward to stance activity during backward walking, and the retractor from stance to swing. This phase switch was due to corresponding change of phasic synaptic inputs from inhibitory to excitatory and vice versa at specific phases of the step cycle. In addition to phasic synaptic input a tonic depolarization of the motor neurons was present. Analysis of changes in stepping velocity during stance showed only a significant correlation to flexor motor neuron activity, but not to that of retractor and depressor motor neurons during forward walking. These results show that different tasks in the stick insect walking system are generated by altering synaptic inputs to specific leg joint motor neurons only.

Directional specificity and encoding of muscle forces and loads by stick insect tibial campaniform sensilla, including receptors with round cuticular caps

In many systems, loads are detected as the resistance to muscle contractions. We studied responses to loads and muscle forces in stick insect tibial campaniform sensilla, including a subgroup of receptors (Group 6B) with unusual round cuticular caps in oval-shaped collars. Loads were applied in different directions and muscle contractions were emulated by applying forces to the tibial flexor muscle tendon (apodeme). All sensilla 1) were maximally sensitive to loads applied in the plane of joint movement and 2) encoded muscle forces but did not discharge to unresisted movements. Identification of 6B sensilla by stimulation of cuticular caps demonstrated that receptor responses were correlated with their morphology. Sensilla with small cuticular collars produced small extracellular potentials, had low thresholds and strong tonic sensitivities that saturated at moderate levels. These receptors could effectively signal sustained loads. The largest spikes, derived from sensilla with large cuticular collars, had strong dynamic sensitivities and signaled a wide range of muscle forces and loads. Tibial sensilla are apparently tuned to produce no responses to inertial forces, as occur in the swing phase of walking. This conclusion is supported by tests in which animals 'stepped' on a compliant surface and sensory discharges only occurred in stance.

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.

Five types of nonspiking interneurons in local pattern-generating circuits of the crayfish swimmeret system

We conducted a quantitative analysis of the different nonspiking interneurons in the local pattern-generating circuits of the crayfish swimmeret system. Within each local circuit, these interneurons control the firing of the power-stroke and return-stroke motor neurons that drive swimmeret movements. Fifty-four of these interneurons were identified during physiological experiments with sharp microelectrodes and filled with dextran Texas red, Neurobiotin, or both. Five types of neurons were identified on the basis of combinations of physiological and anatomical characteristics. Anatomical categories were based on 16 anatomical parameters measured from stacks of confocal images obtained from each neuron. The results support the recognition of two functional classes: inhibitors of power stroke (IPS) and inhibitors of return stroke (IRS). The IPS class of interneuron has three morphological types with similar physiological properties. The IRS class has two morphological types with physiological properties and anatomical features different from the IPS neurons but similar within the class. Three of these five types have not been previously identified. Reviewing the evidence for dye coupling within each type, we conclude that each type of IPS neuron and one type of IRS neuron occur as a single copy in each local pattern-generating circuit. The last IRS type includes neurons that might occur as a dye-coupled pair in each local circuit. Recognition of these different interneurons in the swimmeret pattern-generating circuits leads to a refined model of the local pattern-generating circuit that includes synaptic connections that encode and decode information required for intersegmental coordination of swimmeret movements.

Single perturbations cause sustained changes in searching behavior in stick insects

Stick insects (Cuniculina impigra) possessing only a single front leg perform untargeted stereotypical cyclic searching movements with that leg when it loses contact with the ground. When encountering an object, the animals grasp it. We hypothesized that removal of the object immediately after contact with the leg's tibia would result in a change in searching strategy, i.e. searching movements confined to the former location of the object to regain contact. In our experimental setup, searching movements were restricted to upward and downward movements. After removal of the object, searching movements were continued. However, in post-contact searching, two movement parameters were usually changed: (1) average positions of searching movements were shifted towards the former position of the object; and (2) movement amplitudes were considerably smaller and accompanied by a decrease in cycle period. This confinement of searching movements to the location of contact was interpreted as targeting behavior. All parameters regained initial values after approximately 6 s. Changes in position and amplitudes were independently controlled. Neither of the changes was under visual control, but rather depended on the presence of the trochanteral hairplate, a sensory organ that measures the coxa-trochanter joint position. Changes in average leg position were linked to changes in the ratio of electrical activity in the levator and depressor trochanteris muscles, which were based on altered activity in both or either one of the muscles. Our data demonstrate a switch in a simple behavior that is under local sensory control and may utilize a form of short-term memory.

Lessons for circuit function from large insects: towards understanding the neural basis of motor flexibility

Motor behaviors result from information processing that occurs in multiple neural networks acting at all levels from the initial selection of the behavior to its final generation. A long-standing research interest is how single neural networks can help generate different motor behaviors, that is, the origin of motor flexibility. Modern experimental techniques allow studying neural network activity during the production of multiple motor behaviors. Recent data provide strong evidence that the neural networks controlling insect legs are individually modified in task-dependent and finely tuned fashions. Understanding the mechanistic basis of these neural network modifications will be of particular interest in the upcoming years.

Control of reflex reversal in stick insect walking: effects of intersegmental signals, changes in direction, and optomotor-induced turning

In many animals, the effects of sensory feedback on motor output change during locomotion. These changes can occur as reflex reversals in which sense organs that activate muscles to counter perturbations in posture control instead reinforce movements in walking. The mechanisms underlying these changes are only partially understood. As such, it is unclear whether reflex reversals are modulated when locomotion is adapted, such as during changes in walking direction or in turning movements. We investigated these questions in the stick insect Carausius morosus, where sensory signals from the femoral chordotonal organ are known to produce resistance reflexes at rest but assistive movements during walking. We studied how intersegmental signals from neighboring legs affect the generation of reflex reversals in a semi-intact preparation that allows free leg movement during walking. We found that reflex reversal was enhanced by stepping activity of the ipsilateral neighboring rostral leg, whereas stepping of contralateral legs had no effect. Furthermore, we found that the occurrence of reflex reversals was task-specific: in the front legs of animals with five legs walking, reflex reversal was generated only during forward and not backward walking. Similarly, during optomotor-induced curved walking, reflex reversal occurred only in the middle leg on the inside of the turn and not in the contralateral leg on the outside of the turn. Thus our results show for the first time that the nervous system modulates reflexes in individual legs in the adaptation of walking to specific tasks.

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

This article presents the use of continuous dynamic models in the form of differential equations to describe and predict temporal changes in biological processes and discusses several of its important advantages over discontinuous bistable ones, exemplified on the stick insect walking system. In this system, coordinated locomotion is produced by concerted joint dynamics and interactions on different dynamical scales, which is therefore difficult to understand. Modeling using differential equations possesses, in general, the potential for the inclusion of biological detail, the suitability for simulation, and most importantly, parameter manipulation to make predictions about the system behavior. We will show in this review article how, in case of the stick insect walking system, continuous dynamical system models can help to understand coordinated locomotion.

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.