Biomedical subjects

Holk Cruse

Publications and source records attributed to Holk Cruse.

10 recordsLinked to original sources

PubMed2007

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.

Animals↗

PubMed2007

Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations.

Humans are able to form internal representations of the information they process -- a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.

Humans↗

PubMed2007

Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations.

Modelling the cognitive abilities of humans or animals or building agents that are supposed to behave cognitively requires modelling a memory system that is able to store and retrieve various contents. The content to be stored is assumed to comprise information about more or less invariant environmental objects as well as information about movements. A combination of information about both objects and movements may be called a situation model. Here we focus, in part, on models storing dynamic patterns. In particular, two abilities of humans in representing dynamical systems receive special focus: the capability of representing the acceleration of objects, as can be found in the movement of a pendulum or freely falling objects, and the capability of representing actions of transfer, i.e. motion from one point to another, have been modelled using recurrent networks consisting of input compensation units. In addition, possibilities of combining static and dynamic properties within a single model are studied.

Cognition↗

PubMed2006

Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations.

The control of the legs of a walking hexapod is a complex problem as the legs have three joints each, resulting in a total of 18 degrees of freedom. We addressed this problem using a decentralized architecture termed Walknet, which consists of peripheral pattern generators being coordinated through influences acting mainly between neighbouring legs. Both, the coordinating influences and the local control modules (each acting only on one leg), are biologically inspired. This investigation shows that it is possible to adapt this approach to account for additional biological data by (1) changing the structure of the selector net in a biological plausible way (including force as an analog variable), (2) introducing a biologically motivated coordination influence for coactivation between legs and (3) adding a hypothetical influence between hind and front legs. This network of controllers has been tested using a dynamic simulation. It is able to describe (a) the behaviour of animals walking with one or two legs being amputated and (b) force oscillations that occur in a specific experimental situation, the standing legs of a walking animal.

Amputation, Surgical↗

PubMed2006

Control of swing movement: influences of differently shaped substrate.

Stick insects were studied while walking on different substrates. The trajectories of swing movements are recorded. The starting position of a swing movement is varied in vertical direction and in the direction parallel to body long axis. The trajectories found cannot be predicted by an ANN (Swingnet1) proposed earlier to describe swing movements. However, a modified network (Swingnet2) allows for a satisfying description of the behavioral results. Walking on a narrow treadwheel leads to different swing trajectories compared to walking on a broad treadwheel. These trajectories cannot be described by Swingnet1, too. The form of the swing trajectory may depend on the direction of the force vector by which the leg acts on the ground in the preceding stance. Based on this assumption, an alternative hypothesis (Swingnet3) is proposed that can quantitatively describe all results of our experiment. When stick insects walk from a wide to a narrow substrate, transition between different swing trajectories does not change gradually over time. Rather, the form of the trajectory is determined by the current sensory input of the leg on a step-to-step basis. Finally, four different avoidance reflexes and their implementation into swing movements are investigated and described by a quantitative simulation.

Animals↗

PubMed2005

Leg coordination during turning on an extremely narrow substrate in a bug, Mesocerus marginatus (Heteroptera, Coreidae).

The turning movement of a bug, Mesocerus marginatus, is observed when it walks upside-down below a horizontal beam and, at the end of the beam, performs a sharp turn by 180 degrees . The turn at the end of the beam is accomplished in three to five steps, without strong temporal coordination among legs. During the stance, leg endpoints (tarsi) run through rounded trajectories, rotating to the same side in all legs. During certain phases of the turn, a leg is strongly depressed and the tarsus crosses the midline. Swing movements rotate to the same side as do leg endpoints in stance, in strong contrast to the typical swing movements found in turns or straight walk on a flat surface. Terminal location is found after the search through a trajectory that first moves away from the body and then loops back to find substrate. When a leg during stance has crossed the midline, in the following swing movement the leg may move even stronger on the contralateral side, i.e. is stronger depressed, in contrast to swing movements in normal walking, where the leg is elevated. These results suggest that the animals apply a different control strategy compared to walking and turning on a flat surface.

Animals↗

PubMed2005

A biologically inspired active compliant joint using local positive velocity feedback (LPVF).

Starting from studies which revealed that positive feedback is found in the control system for walking in arthropods, we have constructed a new positive feedback driven joint that can be used for solving compliant motion tasks. We propose two different joint constructions each of which shows passive compliance. Based on these joints we introduce three different local positive velocity feedback (LPVF) controllers and discuss their properties in the context of motion generation in closed kinematic chains. The third circuit named undelayed dLPVF is used for the control of a compliant planar manipulator which turns a crank. Our concept is of highly decentralized nature and follows the idea of embodiment. In our case this means that a process which is controlled by LPVF controllers reveals its nature when the controllers interact with this process.

Animals↗

PubMed2004

Stick insect locomotion in a complex environment: climbing over large gaps.

In a complex environment, animals are challenged by various types of obstacles. This requires the controller of their walking system to be highly flexible. In this study, stick insects were presented with large gaps to cross in order to observe how locomotion can be adapted to challenging environmental situations. Different approaches were used to investigate the sequence of gap-crossing behaviour. A detailed video analysis revealed that gap-crossing behaviour resembles modified walking behaviour with additional step types. The walking sequence is interrupted by an interval of exploration, in which the insect probes the gap space with its antennae and front legs. When reaching the gap, loss of contact of an antenna with the ground does not elicit any observable reactions. In contrast, an initial front leg step into the gap that often follows antennal 'non-contact' evokes slowing down of stance velocity. An ablation experiment showed that the far edge of the gap is detected by tactile antennal stimulation rather than by vision. Initial contact of an antenna or front leg with the far edge of the gap represents a 'point of no return', after which gap crossing is always successfully completed. Finally, flow chart diagrams of the gap-crossing sequence were constructed based on an ethogram of single elements of behaviour. Comparing flow charts for two gap sizes revealed differences in the frequency and succession of these elements, especially during the first part of the sequence.

Animals↗

PubMed1998

Oscillatory network controlling six-legged locomotion. Optimization of model parameters.

The model of a legged locomotory system is optimized to ensure stable motion, reliable with respect to different initial conditions. The cost function suggested is based on the frequency of the model's loss of stability evaluated for randomly chosen initial leg positions. The optimized model can start from the majority of allowed leg configurations, demonstrating stable walking at low and moderate speeds. Furthermore, an acceleration procedure is designed to permit the model to pick up practically every speed and then walk successfully.

Journal Article↗

PubMed1998

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.

Journal Article↗