Stable phase-shift despite quasi-rhythmic movements: a CPG-driven dynamic model of active tactile exploration in an insect

Harischandra N, Krause AF, Dürr V. Stable phase-shift despite quasi-rhythmic movements: a CPG-driven dynamic model of active tactile exploration in an insect. Frontiers in Computational Neuroscience. 2015;9: 107.An essential component of autonomous and flexible behaviour in animals is active exploration of the environment, allowing for perception-guided planning and control of actions. An important sensory system involved is active touch. Here, we introduce a general modelling framework of Central Pattern Generators (CPGs) for movement generation in active tactile exploration behaviour. The CPG consists of two network levels: (i) phase-coupled Hopf oscillators for rhythm generation, and (ii) pattern formation networks for capturing the frequency and phase characteristics of individual joint oscillations. The model captured the natural, quasi-rhythmic joint kinematics as observed in coordinated antennal movements of walking stick insects. Moreover, it successfully produced tactile exploration behaviour on a three-dimensional skeletal model of the insect antennal system with physically realistic parameters. The effect of proprioceptor ablations could be simulated by changing the amplitude and offset parameters of the joint oscillators, only. As in the animal, the movement of both antennal joints was coupled with a stable phase difference, despite the quasi-rhythmicity of the joint angle time courses. We found that the phase-lead of the distal scape-pedicel joint relative to the proximal head-scape joint was essential for producing the natural tactile exploration behaviour and, thus, for tactile efficiency. For realistic movement patterns, the phase-lead could vary within a limited range of 10 to 30 degrees only. Tests with artificial movement patterns strongly suggest that this phase sensitivity is not a matter of the frequency composition of the natural movement pattern. Based on our modelling results, we propose that a constant phase difference is coded into the CPG of the antennal motor system and that proprioceptors are acting locally to regulate the joint movement amplitude

Decoding the mechanisms of gait generation in salamanders by combining neurobiology, modeling and robotics

Vertebrate animals exhibit impressive locomotor skills. These locomotor skills are due to the complex interactions between the environment, the musculo-skeletal system and the central nervous system, in particular the spinal locomotor circuits. We are interested in decoding these interactions in the salamander, a key animal from an evolutionary point of view. It exhibits both swimming and stepping gaits and is faced with the problem of producing efficient propulsive forces using the same musculo-skeletal system in two environments with significant physical differences in density, viscosity and gravitational load. Yet its nervous system remains comparatively simple. Our approach is based on a combination of neurophysiological experiments, numerical modeling at different levels of abstraction, and robotic validation using an amphibious salamander-like robot. This article reviews the current state of our knowledge on salamander locomotion control, and presents how our approach has allowed us to obtain a first conceptual model of the salamander spinal locomotor networks. The model suggests that the salamander locomotor circuit can be seen as a lamprey-like circuit controlling axial movements of the trunk and tail, extended by specialized oscillatory centers controlling limb movements. The interplay between the two types of circuits determines the mode of locomotion under the influence of sensory feedback and descending drive, with stepping gaits at low drive, and swimming at high driv

Computer Simulation of the Neural Control of Locomotion in the Cat and the Salamander

Locomotion is an integral part of a whole range of animal behaviours. The basic rhythm for locomotion in vertebrates has been shown to arise from local networks residing in the spinal cord and these networks are known as central pattern generators (CPG). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping or swimming to varying environmental conditions. Conceptual models of vertebrate locomotion have been constructed using mathematical models of locomotor subsystems based on the neurophysiological evidence obtained primarily in the cat and the salamander, an amphibian with a sprawling posture. Such models provide opportunity for studying the key elements in the transition from aquatic to terrestrial locomotion. Several aspects of locomotor control using the cat or the salamander as an animal model have been investigated employing computer simulations and here we use the same approach to address a number of questions or/and hypotheses related to rhythmic locomotion in quadrupeds. Some of the involved questions are, the role of mechanical linkage during deafferented walking, finding inherent stabilities/instabilities of muscle-joint interactions during normal walking and estimating phase dependent controlability of muscle action over joints. Also we investigate limb and body coordination for different gaits, use of side-stepping in front limbs for turning and the role of sensory feedback in gait generation and transitions in salamanders.      This thesis presents the basics of the biologically realistic models of cat and salamander locomotion and summarizes computational methods in modeling quadruped locomotor subsystems such as CPG, limb muscles and sensory pathways. In the case of cat hind limb, we conclude that the mechanical linkages between the legs play a major role in producing the alternating gait. In another experiment we use the model to identify open-loop linear transfer functions between muscle activations and joint angles while ongoing locomotion. We hypothesize that the musculo-skeletal system for locomotion in animals, at least in cats, operates under critically damped condition.      The 3D model of the salamander is successfully used to mimic locomotion on level ground and in water. We compare the walking gait with the trotting gait in simulations. We also found that for turning, the use of side-stepping alone or in combination with trunk bending is more effective than the use of trunk bending alone. The same model together with a more realistic CPG composed of spiking neurons was used to investigate the role of sensory feedback in gait generation and transition. We found that the proprioceptive sensory inputs are essential in obtaining the walking gait, whereas the trotting gait is more under central (CPG) influence compared to that of the peripheral or sensory feedback.      This thesis work sheds light on understanding the neural control mechanisms behind vertebrate locomotion. Additionally, both neuro-mechanical models can be used for further investigations in finding new control algorithms which give robust, adaptive, efficient and realistic stepping in each leg, which would be advantageous since it can be implemented on a controller of a quadruped-robotic device.This work is Funded by Swedish International Development cooperation Agency (SIDA). QC 2011111

Computer Simulation of the Neural Control of Locomotion in the Cat

Locomotion is one of the most important behaviours and requires interaction between sensors at various levels of the nervous system and the limb muscles of an animal. The basic neural rhythm for locomotion in mammals has been shown to arise from local neural networks residing in the spinal cord and these networks are known as central pattern generators (CPGs). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping to varying environmental conditions. Conceptual models of mammalian locomotion have been constructed using mathematical models of locomotor subsystems based on the abundance of neurophysiological evidence obtained primarily in the cat. Several aspects of locomotor control using the cat as an animal model have been investigated employing computer simulations and here we use the same approach to address number of questions or/and hypotheses related to rhythmic locomotion in quadrupeds. Some of the involve questions are, role of mechanical linkage during deafferented walking, finding inherent stabilities/instabilities of muscle-joint interactions during normal walking, estimating phase dependent controlability of muscle action over joints. This thesis presents the basics of a biologically realistic model of mammalian locomotion and summarises methodological approaches in modelling quadruped locomotor subsystems such as CPGs, limb muscles and sensory pathways. In the first appended article, we extensively discuss the construction details of the three-dimensional computer simulator for the study of the hind leg neuro-musculo-skeletal-control system and its interactions during normal walking of the cat. The simulator with the walking model is programmed in Python scripting language with other supported open source libraries such as Open Dynamics Engine (ODE) for simulating body dynamics and OpenGL for three dimensional graphical representation. We have examined the functionality of the simulator and the walking model by simulating deafferented walking. It was possible to obtain a realistic stepping in the hind legs even without sensory feedback to the two controllers (CPGs) for each leg. We conclude that the mechanical linkages between the legs also play a major role in producing alternating gait. The use of simulations of walking in the cat for gaining insights into more complex interactions between the environment and the neuro-muscular-skeletal system is important especially for questions where a direct neurophysiological experiment can not be performed on a real walking animal. For instance, it is experimentally hard to isolate individual mechanisms without disrupting the natural walking pattern. In the second article, we introduce a different approach where we use the walking model to identify what control is necessary to maintain stability in the musculo-skeletal system. We show that the actions of most of the hindlimb muscles over the joints have an inherent stability during stepping, even without the involvement of proprioceptive feedback mechanisms. In addition, we observe that muscles generating movements in the ankle joint of the hind leg must be controlled by neural mechanisms, which may involve supraspinal structures, over the whole step cycle.QC 2010111

Computer Simulation of the Neural Control of Locomotion in the Cat and the Salamander

Locomotion is an integral part of a whole range of animal behaviours. The basic rhythm for locomotion in vertebrates has been shown to arise from local networks residing in the spinal cord and these networks are known as central pattern generators (CPG). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping or swimming to varying environmental conditions. Conceptual models of vertebrate locomotion have been constructed using mathematical models of locomotor subsystems based on the neurophysiological evidence obtained primarily in the cat and the salamander, an amphibian with a sprawling posture. Such models provide opportunity for studying the key elements in the transition from aquatic to terrestrial locomotion. Several aspects of locomotor control using the cat or the salamander as an animal model have been investigated employing computer simulations and here we use the same approach to address a number of questions or/and hypotheses related to rhythmic locomotion in quadrupeds. Some of the involved questions are, the role of mechanical linkage during deafferented walking, finding inherent stabilities/instabilities of muscle-joint interactions during normal walking and estimating phase dependent controlability of muscle action over joints. Also we investigate limb and body coordination for different gaits, use of side-stepping in front limbs for turning and the role of sensory feedback in gait generation and transitions in salamanders.      This thesis presents the basics of the biologically realistic models of cat and salamander locomotion and summarizes computational methods in modeling quadruped locomotor subsystems such as CPG, limb muscles and sensory pathways. In the case of cat hind limb, we conclude that the mechanical linkages between the legs play a major role in producing the alternating gait. In another experiment we use the model to identify open-loop linear transfer functions between muscle activations and joint angles while ongoing locomotion. We hypothesize that the musculo-skeletal system for locomotion in animals, at least in cats, operates under critically damped condition.      The 3D model of the salamander is successfully used to mimic locomotion on level ground and in water. We compare the walking gait with the trotting gait in simulations. We also found that for turning, the use of side-stepping alone or in combination with trunk bending is more effective than the use of trunk bending alone. The same model together with a more realistic CPG composed of spiking neurons was used to investigate the role of sensory feedback in gait generation and transition. We found that the proprioceptive sensory inputs are essential in obtaining the walking gait, whereas the trotting gait is more under central (CPG) influence compared to that of the peripheral or sensory feedback.      This thesis work sheds light on understanding the neural control mechanisms behind vertebrate locomotion. Additionally, both neuro-mechanical models can be used for further investigations in finding new control algorithms which give robust, adaptive, efficient and realistic stepping in each leg, which would be advantageous since it can be implemented on a controller of a quadruped-robotic device.This work is Funded by Swedish International Development cooperation Agency (SIDA). QC 2011111

Predictions of self-induced mechanoreceptive sensor readings in an insect-inspired active tactile sensing system

Harischandra N, Dürr V. Predictions of self-induced mechanoreceptive sensor readings in an insect-inspired active tactile sensing system. In: Proceedings of the International Symposium on Adaptive Motion of Animals and Machines AMAM2013. 2013: 59-62.In this study, we expand our work on Echo State Network (ESN) based forward models for predicting the expected sensor reading of an insect- inspired active tactile sensor system on a mobile robot. Inputs to the forward model are the motor commands which set the positions of both antennae, and a local proprioceptive signal which measures the vibra tions of the robot platform. The model was successfully used to detect tactile events on each antenna while the robot, is either moved on a straight or curved path with bumps on the floor, or moved with spiked wheels. The latter scenario was chosen to estimate the performance of an active tactile system mounted to a walking robot with rhythmic impacts caused by the legs touching down. By simulated lesion experiments, we found that the local proprioceptive signal plays a major role in discriminating disturbances from tactile events in the proposed forward model. We conclude that such an ESN-based forward model is suitable for prediction and elimination of self-induced stimulation of sensors, in particular of mechanosensors

A forward model for an active tactile sensor using Echo State Networks

Harischandra N, Dürr V. A forward model for an active tactile sensor using Echo State Networks. In: 6th International Symposium on Robotics and Sensors Environments (ROSE-2012). 2012: 103-108.Here, we introduce a forward model designed for predicting the expected reading of a bionic tactile sensor (antenna) mounted onto a wheeled robot. The model was used to distinguish self-generated stimulation from true tactile events to the antenna. An Echo State Network (ESN), a special type of recurrent neural network which is suitable for chaotic time series prediction, is used to implement the forward model. Inputs to the ESN are the motor command which sets the position of the antenna, and a local proprioceptive signal which measures the acceleration of the robot platform. The model can successfully be used to detect a tactile contact on the antenna while the robot is moving along a path with obstacles. Such forward models are good candidates to be used in neural yet simple way to eliminate self-stimulation of sensors of other modalities due to ego-motion

Integrating touch and vision in stick insects and insectoid robots

Hoinville T, Harischandra N, Dürr V. Integrating touch and vision in stick insects and insectoid robots. Presented at the Göttingen Meeting of the German Neuroscience Society 2015, Göttingen

A 3D dynamic model for the study of central drive of antennal movements in insects

Harischandra N, Krause AF, Dürr V. A 3D dynamic model for the study of central drive of antennal movements in insects. Presented at the Göttingen Meeting of the German Neuroscience Society 2015, Göttingen