Josef Schmitz

Walknet—a biologically inspired network to control six-legged walking

To investigate walking we perform experimental studies on animals in parallel with software and hardware simulations of the control structures and the body to be controlled. Therefore, the primary goal of our simulation studies is not so much to develop a technical device, but to develop a system which can be used as a scientific tool to study insect walking. To this end, the animat should copy essential properties of the animals. In this review, we will first describe the basic behavioral properties of hexapod walking, as the are known from stick insects. Then we describe a simple neural network called Walknet which exemplifies these properties and also shows some interesting emergent properties. The latter arise mainly from the use of the physical properties to simplify explicit calculations. The model is simple too, because it uses only static neuronal units. Finally, we present some new behavioral results.

Walking: a complex behavior controlled by simple networks

Understanding how behavior is controlled requires that modeling be combined with behavioral, electrophysiological, and neuroanatomical investigations. One problem in studying motor systems is that they have considerable autonomy; they are not driven solely by inputs. Choosing walking as the object of study is promising because it is a comparably simple and easy-to-elicit behavior, but it exhibits the special feature of most motor behavior—the interaction between central, autonomous components and peripheral, sensory influences. This article reviews the control of walking in stick insects, beginning with behavioral studies of single-leg control and the interleg coordinating mechanisms. These behavioral results are tested and supported by modeling the control system in an artificial neural network computer simulation and a six-legged robot. Supporting neurophysiological results also are considered. Together, the results indicate that the high flexibility and adaptability is based on a simple distributed control structure.

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.

Segment specificity of load signal processing depends on walking direction in the stick insect leg muscle control system.

In terrestrial locomotion, sensory feedback from load sensors is important for altering ongoing motor output on a step-by-step basis. We investigated the influence of load signals from the leg on motoneuron pools of the thorax-coxa (ThC) joint in the stick insect walking system. Load sensors were stimulated during rhythmic, alternating activity in protractor coxae (ProCx) and retractor coxae (RetCx) motoneuron pools. Alternating activity in the segment of interest was induced by mechanical stimulation of the animal or pharmacological activation of the isolated thoracic ganglia. Load signals from the legs altered the timing of ThC motoneuron activity by resetting and entraining the activity of the central rhythm generating network of the ThC joint. In the front and middle legs, load signals induced or promoted RetCx activity and decreased or terminated ProCx activity. In the hindleg, reverse transitions were elicited, with increasing load terminating RetCx and initiating ProCx activity. Studies in semi-intact walking animals showed that the effect of load on the ThC-joint motoneurons depended on walking direction, with increased load promoting the functional stance phase motoneuron pool (in forward walking, RetCx activity; in backward walking, ProCx activity). Thus, we show that modifications of sensory feedback in a locomotor system are related to walking direction. In a final set of ablation experiments, we show that the load influence is mediated by the three groups of trochanteral campaniform sensilla.

Identified nonspiking interneurons in leg reflexes and during walking in the stick insect

In the stick insect Carausius morosus identified nonspiking interneurons (type E4) were investigated in the mesothoracic ganglion during intraand intersegmental reflexes and during searching and walking.In the standing and in the actively moving animal interneurons of type E4 drive the excitatory extensor tibiae motoneurons, up to four excitatory protractor coxae motoneurons, and the common inhibitor 1 motoneuron (Figs. 1–4).In the standing animal a depolarization of this type of interneuron is induced by tactile stimuli to the tarsi of the ipsilateral front, middle and hind legs (Fig. 5). This response precedes and accompanies the observed activation of the affected middle leg motoneurons. The same is true when compensatory leg placement reflexes are elicited by tactile stimuli given to the tarsi of the legs (Fig. 6).During forward walking the membrane potential of interneurons of type E4 is strongly modulated in the step-cycle (Figs.8–10). The peak depolarization occurs at the transition from stance to swing. The oscillations in membrane potential are correlated with the activity profile of the extensor motoneurons and the common inhibitor 1 (Fig. 9).The described properties of interneuron type E4 in the actively behaving animal show that these interneurons are involved in the organization and coordination of the motor output of the proximal leg joints during reflex movements and during walking.

Insect walking is based on a decentralized architecture revealing a simple and robust controller

Control of walking in rugged terrain requires one to incorporate different issues, such as the mechanical properties of legs and muscles, the neuronal control structures for the single leg, the mechanics and neuronal control structures for the coordination between legs, as well as central decisions that are based on external information and on internal states. Walking in predictable environments and fast running, to a large degree, rely on muscle mechanics. Conversely, slow walking in unpredictable terrain, e.g. climbing in rugged structures, has to rely on neuronal systems that monitor and intelligently react to specific properties of the environment. An arthropod model system that shows the latter abilities is the stick insect, based on which this review will be focused. An insect, when moving its six legs, has to control 18 joints, three per leg, and therefore has to control 18 degrees of freedom (d.f.). As the body position in space is determined by 6 d.f. only, there are 12 d.f. open to be selected. Therefore, a fundamental problem is as to how these extra d.f. are controlled. Based mainly on behavioural experiments and simulation studies, but also including neurophysiological results, the following control structures have been revealed. Legs act as basically independent systems. The quasi-rhythmic movement of the individual leg can be described to result from a structure that exploits mechanical coupling of the legs via the ground and the body. Furthermore, neuronally mediated influences act locally between neighbouring legs, leading to the emergence of insect-type gaits. The underlying controller can be described as a free gait controller. Cooperation of the legs being in stance mode is assumed to be based on mechanical coupling plus local positive feedback controllers. These controllers, acting on individual leg joints, transform a passive displacement of a joint into an active movement, generating synergistic assistance reflexes in all mechanically coupled joints. This architecture is summarized in the form of the artificial neural network, Walknet, that is heavily dependent on sensory feedback at the proprioceptive level. Exteroceptive feedback is exploited for global decisions, such as the walking direction and velocity.

An improved electrode design for en passant recording from small nerves

1. A modification of the oil and hook electrode technique for recording extracellularly from fine nerves is described. 2. It uses a fine hook and a plastic tube that can be manipulated independently, and through which a high-viscosity oil or grease may be forced over the nerve. 3. The suitability of the electrode for high-quality and long-term recording is discussed.

The depressor trochanteris motorneurones and their role in the coxo-trochanteral feedback loop in the stick insect carausius morosus

The innervation pattern of the coxal part of the depressor trochanteris muscle is described. This muscle is located inside the coxa cavity and is innervated by motoneurones contained in nerve C2. Serial sections of nerve C2 reveal that nerve C2 contains 3 large neurones (8, 5, and 3 μm in diameter) in addition to many small neurones. In extracellular nerve recordings from nerve C2 3 large spikes could be recorded, which can easily be classified according to their amplitudes. Combined intracellular muscle recordings and extracellular nerve recordings revealed the physiological characteristics of these motoneurones, which are referred to here as the “fast depressor trochanteris” (FDTr) motoneurone and the spontaneously active “slow depressor trochanteris” (SDTr) motoneurone. The third motoneurone could be identified as an inhibitory motoneurone. Because this motoneurone was also found in nerves nl2, nl3, nl5 and in nerve C1 (to the levator trochanteris muscle) it is referred to here as the “common inhibitor” (CI) motoneurone.The hypothesis that the trochanteral hairplate (trHP) is the only effective feedback transducer for the coxo-trochanteral control loop (Schmitz 1984, 1986) is confirmed by the nerve recordings from nerve C2, because no reflex response was measured after ablation of the trHP. In addition, shaving the trHP reduces the activity of the spontaneously active SDTr motoneurone.The frequency responses of the excitatory depressor motoneurones show that the spontaneous activity of the SDTr motoneurone is modulated by the stimulus over a wide range of stimulus frequencies up to 100 Hz and that the FDTr motoneurone is reflexly activated during the same phase of the stimulus as the SDTr motoneurone. Up to 20 Hz the maximum of the motoneurone activity leads the maximum of the movement by about 60 to 80 deg. This shows that nonlinear highpass filter properties of the coxotrochanteral control system, described on the basis of force measurements in an earlier paper (Schmitz 1986), can be found already on the level of the motoneurones.

A biologically inspired controller for hexapod walking: simple solutions by exploiting physical properties.

The locomotor system of slowly walking insects is well suited for coping with highly irregular terrain and therefore might represent a paragon for an artificial six-legged walking machine. Our investigations of the stick insect Carausius morosus indicate that these animals gain their adaptivity and flexibility mainly from the extremely decentralized organization of the control system that generates the leg movements. Neither the movement of a single leg nor the coordination of all six legs (i.e., the gait) appears to be centrally pre-programmed. Thus, instead of using a single, central controller with global knowledge, each leg appears to possess its own controller with only procedural knowledge for the generation of the leg’s movement. This is possible because exploiting the physical properties avoids the need for complete information on the geometry of the system that would be a prerequisite for explicitly solving the problems. Hence, production of the gait is an emergent property of the whole system, in which each of the six single-leg controllers obeys a few simple and local rules in processing state-dependent information about its neighbors.

Control of Walking in the Stick Insect: From Behavior and Physiology to Modeling

Classical engineering approaches to controlling a hexapod walker typically involve a central control instance that implements an abstract optimal gait pattern and relies on additional optimization criteria to generate reference signals for servocontrollers at all the joints. In contrast, the gait of the slow-walking stick insect apparently emerges from an extremely decentralized architecture with separate step pattern generators for each leg, a strong dependence on sensory feedback, and multiple, in part redundant, primarily local interactions among the step pattern generators. Thus, stepping and step coordination do not reflect an explicit specification based on a global optimization using a representation of the system and its environment; instead they emerge from a distributed system and from the complex interaction with the environment. A similarly decentralized control at the level of single leg joints also may explain the control of leg dynamics. Simulations show that negative feedback for control of body height and walking direction combined with positive feedback for generation of propulsion produce a simple, extremely decentralized system that can handle a wide variety of changes in the walking system and its environment. Thus, there is no need for a central controller implementing global optimization. Furthermore, physiological results indicate that the nervous system uses approximate algorithms to achieve the desired behavioral output rather than an explicit, exact solution of the problem. Simulations and implementation of these design principles are being used to test their utility for controlling six-legged walking machines.