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Motor systems in animals are highly dependent on sensory information for optimal control and precision, with mechanosensory feedback from the somatosensory system playing a critical role. These mechanosensory pathways are woven into the descending feedforward pathways and local central pattern generator circuits that control and generate movement, respectively. Somatosensory feedback in mammals and insects, the two animal classes this review touches upon, is complex due to the increased demands that limbed locomotion, weight-bearing, and corrective movements place on sensorimotor control. In this review, we outline the salient features of the proprioceptive and exteroceptive sensory feedback pathways animals rely on for controlling movement and highlight some of the key principles of sensory feedback that are shared across the animal kingdom. 

This review discusses how the nervous system controls the complex body movements keeping animals up and running. In particular, we revisit how research in insects has shed light on motor control principles that govern movements across the animal kingdom. Starting with the organization and evolution of the insect nervous system, we discuss insights into the neuronal control of behaviors varying in complexity, including escape, flight, crawling, walking, grooming, and courtship. These behaviors share specific control features. For instance, central pattern-generating circuits (CPG), which reside in proximity to the motor neurons and muscles, support the generation of rhythmic motor activity. The number of CPGs involved depends on the complexity of the motor apparatus controlled, such as wing pairs for flight or six pairs of multisegmented legs for walking. The different control architectures are introduced with respect to their organization, topology, and operation. Sensory feedback plays a pivotal role in shaping CPG activity into a functional, well-coordinated motor output. The activity of motor circuits is orchestrated by descending neurons connecting the brain to the ventral nerve cord or spinal cord, which initiate, maintain, modulate, and terminate different actions. We therefore discuss the current understanding of descending control and the contributions of individual, command-like descending neurons and population codes. To highlight how insights in insects help discover fundamental motor control principles, we cross-reference findings from other animals, particularly vertebrates. In addition, we discuss methodological advances that enabled breakthroughs in motor control research, including neurogenetics and connectomics, and discuss key open questions. 

Summary Animals need to fine-control the speed and direction of locomotion to navigate complex and dynamic environments. To achieve this, they integrate multimodal sensory inputs with their internal drive to constantly adjust their motor output. This integration involves the interplay of neuronal populations across different hierarchical levels along the sensorimotor axis – from sensory, central, and modulatory neurons in the brain to descending neurons and motor networks in the nerve cord. Here, we characterize two populations of neurons that control distinct aspects of walking on different hierarchical levels inDrosophila. First, we usein-vivoelectrophysiological recordings to demonstrate that moonwalker descending neurons (MDN) integrate antennal touch to drive changes in walking direction from forward to backward. Second, we establish DopaMeander as an important component in the control of forward walking through a combination of optogenetic activation, silencing, connectomics, andin-vivorecordings. These dopaminergic modulatory neurons drive forward walking with increased turning, and the activity of individual neurons is correlated with ipsiversive turning. Hence, MDN and DopaMeander control opposite regimes of walking on different hierarchical levels. Computational models reveal that their activity predicts key parameters of spontaneous walking. Moreover, we find that both MDN and DopaMeander are gated out during flight. This suggests that neuronal populations across levels of control are modulated by the behavioral state to minimize cross-talk between motor programs. 

Campaniform sensilla (CS) are mechanosensors embedded in the cuticle of insects. They are often found at locations near the joints of leg segments. On legs, CS are generally considered to respond directionally to cuticle bending during legged locomotion. It is currently unclear how CS locations affect strain levels at the CS, but this information is crucial for understanding how CS respond to stimuli. Here we present a parametric finite element model of the femoral CS field forDrosophilahind legs with 12 general and seven CS-specific parameters each. This model allows testing how changes in CS location, orientation and material property affect strain levels at each CS. We used experimentally acquired kinematic data and computed ground reaction forces to simulatein vivo-like forward stepping. The displacements found in this study at the physiological CS field location near the trochanter–femur joint are smaller than those necessary for conformation changes of ion channels involved in signal elicitation. Also, variation of material properties of the CS had little influence on displacement magnitudes at the CS cap where the sensory neuron attaches. Thus, our results indicate that ground reaction forces alone are unlikely to serve CS field activation during forward walking. 

ABSTRACT Insects use walking behavior in a large number of contexts, such as exploration, foraging, escape and pursuit, or migration. A lot is known about how nervous systems produce this behavior in general and also how certain parameters vary with regard to walking direction or speed, for instance. An aspect that has not received much attention is whether and how walking behavior varies across individuals of a particular species. To address this, we created a large corpus of kinematic walking data of many individuals of the fruit fly Drosophila. We only selected instances of straight walking in a narrow range of walking speeds to minimize the influence of high-level parameters, such as turning and walking speed, aiming to uncover more subtle aspects of variability. Using high-speed videography and automated annotation, we captured the positions of the six leg tips for thousands of steps and used principal components analysis to characterize the postural space individuals used during walking. Our analysis shows that the largest part of walking kinematics can be described by five principal components (PCs). Separation of these five PCs into a 2D and a 3D subspace divided the description of walking behavior into invariant features shared across individuals and features that relate to the specifics of individuals; the latter features can be regarded as idiosyncrasies. We also demonstrate that this approach can detect the effects of experimental interventions in an unbiased manner and that general aspects of individuality, such as the individual walking posture, can be described. 

Abstract For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, “biomimetic” robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. In this work, we report the development and validation of the robot Drosophibot II, a meso-scale robotic model of an adult fruit fly, Drosophila melanogaster. This robot is novel for its close attention to the kinematics and dynamics of Drosophila, an increasingly important model of legged locomotion. Each leg’s proportions and degrees of freedom have been modeled after Drosophila 3D pose estimation data. We developed a program to automatically solve the inverse kinematics necessary for walking and solve the inverse dynamics necessary for mechatronic design. By applying this solver to a fly-scale body structure, we demonstrate that the robot’s dynamics fit those modeled for the fly. We validate the robot’s ability to walk forward and backward via open-loop straight line walking with biologically inspired foot trajectories. This robot will be used to test biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which will increase our understanding of how the nervous system controls legged locomotion. 

Walking is the most common form of how animals move on land. The model organismDrosophila melanogasterhas become increasingly popular for studying how the nervous system controls behavior in general and walking in particular. Despite recent advances in tracking and modeling leg movements of walkingDrosophilain 3D, there are still gaps in knowledge about the biomechanics of leg joints due to the tiny size of fruit flies. For instance, the natural alignment of joint rotational axes was largely neglected in previous kinematic analyses. In this study, we therefore present a detailed kinematic leg model in which not only the segment lengths but also the main rotational axes of the joints were derived from anatomical landmarks, namely, the joint condyles. Our model with natural oblique joint axes is able to adapt to the 3D leg postures of straight and forward walking fruit flies with high accuracy. When we compared our model to an orthogonalized version, we observed that our model showed a smaller error as well as differences in the used range of motion (ROM), highlighting the advantages of modeling natural rotational axes alignment for the study of joint kinematics. We further found that the kinematic profiles of front, middle, and hind legs differed in the number of required degrees of freedom as well as their contributions to stepping, time courses of joint angles, and ROM. Our findings provide deeper insights into the joint kinematics of walking inDrosophila, and, additionally, will help to develop dynamical, musculoskeletal, and neuromechanical simulations. 

ABSTRACT The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks – i.e. the local neuronal circuitry controlling motor output and movements – within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible 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.