Intersegmental coordination of cockroach locomotion: adaptive control of centrally coupled pattern generator circuits

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Animal behavior emerges from an interaction between neural network dynamics, musculoskeletal properties and the physical environment. Accessing and understanding the interplay between these elements requires the development of integrative and morphologically realistic neuromechanical simulations. Here we present NeuroMechFly, a data-driven model of the widely studied organism, Drosophila melanogaster. NeuroMechFly combines four independent computational modules: a physics-based simulation environment, a biomechanical exoskeleton, muscle models and neural network controllers. To enable use cases, we first define the minimum degrees of freedom of the leg from real three-dimensional kinematic measurements during walking and grooming. Then, we show how, by replaying these behaviors in the simulator, one can predict otherwise unmeasured torques and contact forces. Finally, we leverage NeuroMechFly's full neuromechanical capacity to discover neural networks and muscle parameters that drive locomotor gaits optimized for speed and stability. Thus, NeuroMechFly can increase our understanding of how behaviors emerge from interactions between complex neuromechanical systems and their physical surroundings.

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.

Body and Tail Coordination in the Bluespot Salamander (Ambystoma laterale) During Limb Regeneration

Animals are incredibly good at adapting to changes in their environment, a trait envied by most roboticists. Many animals use different gaits to seamlessly transition between land and water and move through non-uniform terrains. In addition to adjusting to changes in their environment, animals can adjust their locomotion to deal with missing or regenerating limbs. Salamanders are an amphibious group of animals that can regenerate limbs, tails, and even parts of the spinal cord in some species. After the loss of a limb, the salamander successfully adjusts to constantly changing morphology as it regenerates the missing part. This quality is of particular interest to roboticists looking to design devices that can adapt to missing or malfunctioning components. While walking, an intact salamander uses its limbs, body, and tail to propel itself along the ground. Its body and tail are coordinated in a distinctive wave-like pattern. Understanding how their bending kinematics change as they regrow lost limbs would provide important information to roboticists designing amphibious machines meant to navigate through unpredictable and diverse terrain. We amputated both hindlimbs of blue-spotted salamanders (Ambystoma laterale) and measured their body and tail kinematics as the limbs regenerated. We quantified the change in the body wave over time and compared them to an amphibious fish species, Polypterus senegalus. We found that salamanders in the early stages of regeneration shift their kinematics, mostly around their pectoral girdle, where there is a local increase in undulation frequency. Amputated salamanders also show a reduced range of preferred walking speeds and an increase in the number of bending waves along the body. This work could assist roboticists working on terrestrial locomotion and water to land transitions.

From Motor-Output to Connectivity: An In-Depth Study of in-vitro Rhythmic Patterns in the Cockroach Periplaneta americana

The cockroach is an established model in the study of locomotion control. While previous work has offered important insights into the interplay among brain commands, thoracic central pattern generators, and the sensory feedback that shapes their motor output, there remains a need for a detailed description of the central pattern generators' motor output and their underlying connectivity scheme. To this end, we monitored pilocarpine-induced activity of levator and depressor motoneurons in two types of novel in-vitro cockroach preparations: isolated thoracic ganglia and a whole-chain preparation comprising the thoracic ganglia and the subesophageal ganglion. Our data analyses focused on the motoneuron firing patterns and the coordination among motoneuron types in the network. The burstiness and rhythmicity of the motoneurons were monitored, and phase relations, coherence, coupling strength, and frequency-dependent variability were analyzed. These parameters were all measured and compared among network units both within each preparation and among the preparations. Here, we report differences among the isolated ganglia, including asymmetries in phase and coupling strength, which indicate that they are wired to serve different functions. We also describe the intrinsic default gait and a frequency-dependent coordination. The depressor motoneurons showed mostly similar characteristics throughout the network regardless of interganglia connectivity; whereas the characteristics of the levator motoneurons activity were mostly ganglion-dependent, and influenced by the presence of interganglia connectivity. Asymmetries were also found between the anterior and posterior homolog parts of the thoracic network, as well as between ascending and descending connections. Our analyses further discover a frequency-dependent inversion of the interganglia coordination from alternations between ipsilateral homolog oscillators to simultaneous activity. We present a detailed scheme of the network couplings, formulate coupling rules, and review a previously suggested model of connectivity in light of our new findings. Our data support the notion that the inter-hemiganglia coordination derives from the levator networks and their coupling with local depressor interneurons. Our findings also support a dominant role of the metathoracic ganglion and its ascending output in governing the anterior ganglia motor output during locomotion in the behaving animal.

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.

Information-based centralization of locomotion in animals and robots

The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in freely running cockroaches. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle's leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems.

Unravelling intra- and intersegmental neuronal connectivity between central pattern generating networks in a multi-legged locomotor system

Animal walking results from a complex interplay of central pattern generating networks (CPGs), local sensory signals expressing position, velocity and forces generated in the legs, and coordinating signals between neighboring legs. In particular, the CPGs control the activity of motoneuron (MN) pools which drive the muscles of the individual legs and are thereby responsible for the generation of rhythmic leg movements. The rhythmic activity of the CPGs as well as their connectivity can be modified by the aforementioned sensory signals. However, the precise nature of the interaction between the CPGs and these sensory signals has remained generally largely unknown. Experimental methods aiming at finding out details of these interactions often apply cholinergic agonists such as pilocarpine in order to induce rhythmic activity in the CPGs. Using this general approach, we removed the influence of sensory signals and investigated the putative connections between CPGs controlling the upward/downward movement in the different legs of the stick insect. The experimental data, i.e. the measured MN activities, underwent connectivity analysis using Dynamic Causal Modelling (DCM). This method can uncover the underlying coupling structure and strength between pairs of segmental CPGs. For the analysis we set up different coupling schemes (models) for DCM and compared them using Bayesian Model Selection (BMS). Models with contralateral connections in each segment and ipsilateral connections on both sides, as well as the coupling from the meta- to the ipsilateral prothoracic ganglion were preferred by BMS to all other types of models tested. Moreover, the intrasegmental coupling strength in the mesothoracic ganglion was the strongest and most stable in all three ganglia.

The Metastability of the Double-Tripod Gait in Locust Locomotion

Insect locomotion represents a fundamental example of neuronal oscillating circuits generating different motor patterns or gaits by controlling their phase coordination. Walking gaits are assumed to represent stable states of the system, often modeled as coupled oscillators. This view is challenged, however, by recent experimental observations, in which in vitro locust preparations consistently converged to synchronous rhythms (all legs oscillating as one), a locomotive pattern never seen in vivo. To reconcile this inconsistency, we developed a modeling framework to capture the trade-off between the two competing mechanisms: the endogenous neuronal circuitry, expressed in vitro, and the feedback mechanisms from sensory and descending inputs, active only in vivo. We show that the ubiquitously observed double-tripod walking gait emerges precisely from this balance. The outcome is a short-lived meta-stable double-tripod gait, which transitions and alternates with stable idling, thus recovering the observed intermittent bouts of locomotion, typical of many insects' locomotion behavior.

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Isolated in vitro locust preparations indicate that idling is a stable fictive gait

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This is in contrast to the dominant in vivo locomotive pattern (i.e., double tripod)

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Hence functional locomotion behavior is dependent on descending and sensory inputs

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The presented model generates intermittent double-tripod bouts as seen empirically

Static stability predicts the continuum of interleg coordination patterns in Drosophila

During walking, insects must coordinate the movements of their six legs for efficient locomotion. This interleg coordination is speed dependent: fast walking in insects is associated with tripod coordination patterns, whereas slow walking is associated with more variable, tetrapod-like patterns. To date, however, there has been no comprehensive explanation as to why these speed-dependent shifts in interleg coordination should occur in insects. Tripod coordination would be sufficient at low walking speeds. The fact that insects use a different interleg coordination pattern at lower speeds suggests that it is more optimal or advantageous at these speeds. Furthermore, previous studies focused on discrete tripod and tetrapod coordination patterns. Experimental data, however, suggest that changes observed in interleg coordination are part of a speed-dependent spectrum. Here, we explore these issues in relation to static stability as an important aspect for interleg coordination in Drosophila We created a model that uses basic experimentally measured parameters in fruit flies to find the interleg phase relationships that maximize stability for a given walking speed. The model predicted a continuum of interleg coordination patterns spanning the complete range of walking speeds as well as an anteriorly directed swing phase progression. Furthermore, for low walking speeds, the model predicted tetrapod-like patterns to be most stable, whereas at high walking speeds, tripod coordination emerged as most optimal. Finally, we validated the basic assumption of a continuum of interleg coordination patterns in a large set of experimental data from walking fruit flies and compared these data with the model-based predictions.

Calcium imaging of CPG-evoked activity in efferent neurons of the stick insect

The stick insect is a well-established experimental animal to study the neural basis of walking. Here, we introduce a preparation that allows combining calcium imaging in efferent neurons with electrophysiological recordings of motor neuron activity in the stick insect thoracic nerve cord. The intracellular free calcium concentration in middle leg retractor coxae motor neurons and modulatory octopaminergic DUM neurons was monitored after backfilling lateral nerve nl5 that contains the axons of these neurons with the calcium indicator Oregon Green BAPTA-1. Rhythmic spike activity in retractor and protractor motor neurons was evoked by pharmacological activation of central pattern generating neuronal networks and recorded extracellularly from lateral nerves. A primary goal of this study was to investigate whether changes in the intracellular free calcium concentration observed in motor neurons during oscillatory activity depend on action potentials. We show that rhythmic spike activity in leg motor neurons induced either pharmacologically or by tactile stimulation of the animal is accompanied by a synchronous modulation in the intracellular free calcium concentration. Calcium oscillations in motor neurons do not appear to depend on calcium influx through voltage-sensitive calcium channels that are gated by action potentials because Calcium oscillations persist after pharmacologically blocking action potentials in the motor neurons. Calcium oscillations were also apparent in the modulatory DUM neurons innervating the same leg muscle. However, the timing of calcium oscillations varied not only between DUM neurons and motor neurons, but also among different DUM neurons. Therefore, we conclude that the motor neurons and the different DUM neurons receive independent central drive.

Nitric oxide-mediated intersegmental modulation of cycle frequency in the crayfish swimmeret system

Crayfish swimmerets are paired appendages located on the ventral side of each abdominal segment that show rhythmic beating during forward swimming produced by central pattern generators in most abdominal segments. For animals with multiple body segments and limbs, intersegmental coordination of central pattern generators in each segment is crucial for the production of effective movements. Here we develop a novel pharmacological approach to analyse intersegmental modulation of swimmeret rhythm by selectively elevating nitric oxide levels and reducing them with pharmacological agents, in specific ganglia. Bath application of L-arginine, the substrate NO synthesis, increased the cyclical spike responses of the power-stroke motor neurons. By contrast the NOS inhibitor, L-NAME decreased them. To determine the role of the different local centres in producing and controlling the swimmeret rhythm, these two drugs were applied locally to two separate ganglia following bath application of carbachol. Results revealed that there was both ascending and descending intersegmental modulation of cycle frequency of the swimmeret rhythm in the abdominal ganglia and that synchrony of cyclical activity between segments of segments was maintained. We also found that there were gradients in the strength effectiveness in modulation, that ascending modulation of the swimmeret rhythm was stronger than descending modulation.

Summary: We develop a novel pharmacological approach using a nitric oxide donor and a nitric oxide synthase inhibitor to analyse modulation and segmental synchrony in the swimmeret rhythm of the crayfish.

Kinematic patterns while walking on a slope at different speeds

During walking, the elevation angles of the thigh, shank, and foot (i.e., the angle between the segment and the vertical) covary along a characteristic loop constrained on a plane. Here, we investigate how the shape of the loop and the orientation of the plane, which reflect the intersegmental coordination, change with the slope of the terrain and the speed of progression. Ten subjects walked on an inclined treadmill at different slopes (between -9° and +9°) and speeds (from 0.56 to 2.22 m/s). A principal component analysis was performed on the covariance matrix of the thigh, shank, and foot elevation angles. At each slope and speed, the variance accounted for by the two principal components was >99%, indicating that the planar covariation is maintained. The two principal components can be associated to the limb orientation (PC1*) and the limb length (PC2*). At low walking speeds, changes in the intersegmental coordination across slopes are characterized mainly by a change in the orientation of the covariation plane and in PC2* and to a lesser extent, by a change in PC1*. As speed increases, changes in the intersegmental coordination across slopes are more related to a change in PC1 *, with limited changes in the orientation of the plane and in PC 2*. Our results show that the kinematic patterns highly depend on both slope and speed. NEW & NOTEWORTHY In this paper, changes in the lower-limb intersegmental coordination during walking with slope and speed are linked to changes in the trajectory of the body center of mass. Modifications in the kinematic pattern with slope depend on speed: at slow speeds, the net vertical displacement of the body during each step is related to changes in limb length and orientation. When speed increases, the vertical displacement is mostly related to a change in limb orientation.

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.

Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task). In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms and control strategies to achieve adaptive interlimb coordination and the design principle for the control system of legged robots.

Intra- and intersegmental influences among central pattern generating networks in the walking system of the stick insect

To efficiently move around, animals need to coordinate their limbs. Proper, context-dependent coupling among the neural networks underlying leg movement is necessary for generating intersegmental coordination. In the slow-walking stick insect, local sensory information is very important for shaping coordination. However, central coupling mechanisms among segmental central pattern generators (CPGs) may also contribute to this. Here, we analyzed the interactions between contralateral networks that drive the depressor trochanteris muscle of the legs in both isolated and interconnected deafferented thoracic ganglia of the stick insect on application of pilocarpine, a muscarinic acetylcholine receptor agonist. Our results show that depressor CPG activity is only weakly coupled between all segments. Intrasegmental phase relationships differ between the three isolated ganglia, and they are modified and stabilized when ganglia are interconnected. However, the coordination patterns that emerge do not resemble those observed during walking. Our findings are in line with recent studies and highlight the influence of sensory input on coordination in slowly walking insects. Finally, as a direct interaction between depressor CPG networks and contralateral motoneurons could not be observed, we hypothesize that coupling is based on interactions at the level of CPG interneurons.NEW & NOTEWORTHY Maintaining functional interleg coordination is vitally important as animals locomote through changing environments. The relative importance of central mechanisms vs. sensory feedback in this process is not well understood. We analyzed coordination among the neural networks generating leg movements in stick insect preparations lacking phasic sensory feedback. Under these conditions, the networks governing different legs were only weakly coupled. In stick insect, central connections alone are thus insufficient to produce the leg coordination observed behaviorally.

Design process and tools for dynamic neuromechanical models and robot controllers

We present a serial design process with associated tools to select parameter values for a posture and locomotion controller for simulation of a robot. The controller is constructed from dynamic neuron and synapse models and simulated with the open-source neuromechanical simulator AnimatLab 2. Each joint has a central pattern generator (CPG), whose neurons possess persistent sodium channels. The CPG rhythmically inhibits motor neurons that control the servomotor's velocity. Sensory information coordinates the joints in the leg into a cohesive stepping motion. The parameter value design process is intended to run on a desktop computer, and has three steps. First, our tool FEEDBACKDESIGN uses classical control methods to find neural and synaptic parameter values that stably and robustly control servomotor output. This method is fast, testing over 100 parameter value variations per minute. Next, our tool CPGDESIGN generates bifurcation diagrams and phase response curves for the CPG model. This reveals neural and synaptic parameter values that produce robust oscillation cycles, whose phase can be rapidly entrained to sensory feedback. It also designs the synaptic conductance of inter-joint pathways. Finally, to understand sensitivity to parameters and how descending commands affect a leg's stepping motion, our tool SIMSCAN runs batches of neuromechanical simulations with specified parameter values, which is useful for searching the parameter space of a complicated simulation. These design tools are demonstrated on a simulation of a robot, but may be applied to neuromechanical animal models or physical robots as well.

Rigidity and Flexibility: The Central Basis of Inter-Leg Coordination in the Locust

Many motor behaviors, and specifically locomotion, are the product of an intricate interplay between neuronal oscillators known as central pattern generators (CPGs), descending central commands, and sensory feedback loops. The relative contribution of each of these components to the final behavior determines the trade-off between fixed movements and those that are carefully adapted to the environment. Here we sought to decipher the endogenous, default, motor output of the CPG network controlling the locust legs, in the absence of any sensory or descending influences. We induced rhythmic activity in the leg CPGs in isolated nervous system preparations, using different application procedures of the muscarinic agonist pilocarpine. We found that the three thoracic ganglia, each controlling a pair of legs, have different inherent bilateral coupling. Furthermore, we found that the pharmacological activation of one ganglion is sufficient to induce activity in the other, untreated, ganglia. Each ganglion was thus capable to impart its own bilateral inherent pattern onto the other ganglia via a tight synchrony among the ipsilateral CPGs. By cutting a connective and severing the lateral-longitudinal connections, we were able to uncouple the oscillators’ activity. While the bilateral connections demonstrated a high modularity, the ipsilateral CPGs maintained a strict synchronized activity. These findings suggest that the central infrastructure behind locust walking features both rigid elements, which presumably support the generation of stereotypic orchestrated leg movements, and flexible elements, which might provide the central basis for adaptations to the environment and to higher motor commands.

Endogenous rhythm and pattern-generating circuit interactions in cockroach motor centres

Cockroaches are rapid and stable runners whose gaits emerge from the intricate, and not fully resolved, interplay between endogenous oscillatory pattern-generating networks and sensory feedback that shapes their rhythmic output. Here we studied the endogenous motor output of a brainless, deafferented preparation. We monitored the pilocarpine-induced rhythmic activity of levator and depressor motor neurons in the mesothoracic and metathoracic segments in order to reveal the oscillatory networks’ architecture and interactions. Data analyses included phase relations, latencies between and overlaps of rhythmic bursts, spike frequencies, and the dependence of these parameters on cycle frequency. We found that, overall, ipsilateral connections are stronger than contralateral ones. Our findings revealed asymmetries in connectivity among the different ganglia, in which meta-to-mesothoracic ascending coupling is stronger than meso-to-metathoracic descending coupling. Within-ganglion coupling between the metathoracic hemiganglia is stronger than that in the mesothoracic ganglion. We also report differences in the role and mode of operation of homologue network units (manifested by levator and depressor nerve activity). Many observed characteristics are similar to those exhibited by intact animals, suggesting a dominant role for feedforward control in cockroach locomotion. Based on these data we posit a connectivity scheme among components of the locomotion pattern generating system.

Summary: Detailed analysis of fictive motor patterns unveils endogenous characteristics of the cockroach thoracic locomotion control networks and their interrelations and enables an explanatory connectivity model.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Intersegmental coupling and recovery from perturbations in freely running cockroaches

Cockroaches are remarkably stable runners, exhibiting rapid recovery from external perturbations. To uncover the mechanisms behind this important behavioral trait, we recorded leg kinematics of freely running animals in both undisturbed and perturbed trials. Functional coupling underlying inter-leg coordination was monitored before and during localized perturbations, which were applied to single legs via magnetic impulses. The resulting transient effects on all legs and the recovery times to normal pre-perturbation kinematics were studied. We estimated coupling architecture and strength by fitting experimental data to a six-leg-unit phase oscillator model. Using maximum-likelihood techniques, we found that a network with nearest-neighbor inter-leg coupling best fitted the data and that, although coupling strengths vary among preparations, the overall inputs entering each leg are approximately balanced and consistent. Simulations of models with different coupling strengths encountering perturbations suggest that the coupling schemes estimated from our experiments allow animals relatively fast and uniform recoveries from perturbations.

Instantaneous kinematic phase reflects neuromechanical response to lateral perturbations of running cockroaches

Instantaneous kinematic phase calculation allows the development of reduced-order oscillator models useful in generating hypotheses of neuromechanical control. When perturbed, changes in instantaneous kinematic phase and frequency of rhythmic movements can provide details of movement and evidence for neural feedback to a system-level neural oscillator with a time resolution not possible with traditional approaches. We elicited an escape response in cockroaches (Blaberus discoidalis) that ran onto a movable cart accelerated laterally with respect to the animals' motion causing a perturbation. The specific impulse imposed on animals (0.50 [Formula: see text] 0.04 m s[Formula: see text]; mean, SD) was nearly twice their forward speed (0.25 [Formula: see text] 0.06 m s[Formula: see text]. Instantaneous residual phase computed from kinematic phase remained constant for 110 ms after the onset of perturbation, but then decreased representing a decrease in stride frequency. Results from direct muscle action potential recordings supported kinematic phase results in showing that recovery begins with self-stabilizing mechanical feedback followed by neural feedback to an abstracted neural oscillator or central pattern generator. Trials fell into two classes of forward velocity changes, while exhibiting statistically indistinguishable frequency changes. Animals pulled away from the side with front and hind legs of the tripod in stance recovered heading within 300 ms, whereas animals that only had a middle leg of the tripod resisting the pull did not recover within this period. Animals with eight or more legs might be more robust to lateral perturbations than hexapods.

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

Central drive and proprioceptive control of antennal movements in the walking stick insect

In terrestrial locomotion, active touch sensing is an important source of near-range information. Walking stick insects show active tactile exploration behaviour by continuously sampling the ambient space with their antennae. Here, we identify central and proprioceptive contributions to the control of this behaviour. First, we investigate the potential role of synaptic drive to central neural networks using pilocarpine, an agonist of muscarinic acetylcholine receptors. In an in situ preparation, pilocarpine induced rhythmic antennal movements with a persisting pattern of inter-joint coordination, matching that seen in intact walking animals, albeit with lower cycle frequency. After de-cerebration, stick insects were still able to walk but no longer moved their antennae during walking. Here, pilocarpine still induced antennal movement, suggesting that synaptic drive to central neural networks involved in antennal movement generation occurred in the brain and not in the suboesophageal ganglion. During intact walking, these networks are likely to receive activation by ascending input. Second, we show persistent coupling of both antennal joints during intact walking, with the distal scape-pedicel joint (SP) always leading the proximal head-scape joint (HS). Ablation of joint proprioceptors had no effect on this overall pattern of inter-joint coordination but could affect the magnitude of the phase-lag. Third, we revise the description of antennal hair fields and show that complete ablation of all seven hair fields strongly affects antennal movements. Ablating dorsal hair fields mainly affected the working-ranges of antennal joints: Ablation of the dorso-medial pedicellar hair plate caused a ventral shift of the SP working-range. Ablation of the dorsal scapal hair plate considerably expanded the dorsal HS working-range, and, in combination with ablation of pedicellar hair fields, increased the SP working-range, too. We conclude that the working-ranges of both joints are under proprioceptive control of dorsal antennal hair fields. Thus, both synaptic drive to central neural networks and proprioceptive feedback are involved in the control of active tactile exploration behaviour in stick insects.

Spikes alone do not behavior make: why neuroscience needs biomechanics

Neural circuits do not function in isolation; they interact with the physical world, accepting sensory inputs and producing outputs via muscles. Since both these pathways are constrained by physics, the activity of neural circuits can only be understood by considering biomechanics of muscles, bodies, and the exterior world. We discuss how animal bodies have natural stable motions that require relatively little activation or control from the nervous system. The nervous system can substantially alter these motions, by subtly changing mechanical properties such as body or leg stiffness. Mechanics can also provide robustness to perturbations without sensory reflexes. By considering a complete neuromechanical system, neuroscientists and biomechanicians together can provide a more integrated view of neural circuitry and behavior.