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

2017 ◽  
Vol 118 (4) ◽  
pp. 2296-2310 ◽  
Author(s):  
Charalampos Mantziaris ◽  
Till Bockemühl ◽  
Philip Holmes ◽  
Anke Borgmann ◽  
Silvia Daun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sensory Feedback ◽  
Muscarinic Acetylcholine Receptor ◽  
Central Pattern Generators ◽  
Sensory Information ◽  
Direct Interaction ◽  
Stick Insect ◽  
Weakly Coupled ◽  
The Neural Networks ◽  
Pattern Generators

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.

scholarly journals Increasing viscosity helps explain locomotor control in swimming Polypterus seneglus

2021 ◽  
Author(s):  
K Lutek ◽  
E M Standen
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Muscle Activation ◽  
Central Pattern Generators ◽  
Sensory Information ◽  
High Viscosity ◽  
Control Input ◽  
Polypterus Senegalus ◽  
Similar Motor ◽  
Pattern Generators

Abstract Locomotion relies on the successful integration of sensory information to adjust brain commands and basic motor rhythms created by central pattern generators. It is not clearly understood how altering the sensory environment impacts control of locomotion. In an aquatic environment, mechanical sensory feedback to the animal can be readily altered by adjusting water viscosity. Computer modeling of fish swimming systems show that, without sensory feedback, high viscosity systems dampen kinematic output despite similar motor control input. We recorded muscle activity and kinematics of six Polypterus senegalus in four different viscosities of water from 1 cP (normal water) to 40 cP. In high viscosity, P. senegalus exhibit increased body curvature, body wavespeed and body and pectoral fin frequency during swimming. These changes are the result of increased muscle activation intensity and maintain voluntary swimming speed. Unlike the sensory deprived model, intact sensory feedback allows fish to adjust swimming motor control and kinematic output in high viscous water but maintain typical swimming coordination.

Intersegmental Coordination of Walking Movements in Stick Insects

2005 ◽  
Vol 93 (3) ◽  
pp. 1255-1265 ◽  
Author(s):  
Björn Ch. Ludwar ◽  
Marie L. Göritz ◽  
Joachim Schmidt
Keyword(s):  
Motor Pattern ◽  
Central Pattern Generators ◽  
Stick Insect ◽  
Neural Mechanisms ◽  
Chordotonal Organ ◽  
Adjacent Segment ◽  
Body Segments ◽  
Stick Insects ◽  
Pattern Generators ◽  
Leg Movements

Locomotion requires the coordination of movements across body segments, which in walking animals is expressed as gaits. We studied the underlying neural mechanisms of this coordination in a semi-intact walking preparation of the stick insect Carausius morosus. During walking of a single front leg on a treadmill, leg motoneuron (MN) activity tonically increased and became rhythmically modulated in the ipsilateral deafferented and deefferented mesothoracic (middle leg) ganglion. The pattern of modulation was correlated with the front leg cycle and specific for a given MN pool, although it was not consistent with functional leg movements for all MN pools. In an isolated preparation of a pair of ganglia, where one ganglion was made rhythmically active by application of pilocarpine, we found no evidence for coupling between segmental central pattern generators (CPGs) that could account for the modulation of MN activity observed in the semi-intact walking preparation. However, a third preparation provided evidence that signals from the front leg's femoral chordotonal organ (fCO) influenced activity of ipsilateral MNs in the adjacent mesothoracic ganglion. These intersegmental signals could be partially responsible for the observed MN activity modulation during front leg walking. While afferent signals from a single walking front leg modulate the activity of MNs in the adjacent segment, additional afferent signals, local or from contralateral or posterior legs, might be necessary to produce the functional motor pattern observed in freely walking animals.

Sensory Control and Organization of Neural Networks Mediating Coordination of Multisegmental Organs for Locomotion

2005 ◽  
Vol 93 (3) ◽  
pp. 1127-1135 ◽  
Author(s):  
Ansgar Büschges
Keyword(s):  
Neural Networks ◽  
Nervous System ◽  
Sensory Neurons ◽  
Sensory Feedback ◽  
Stick Insect ◽  
Sensory Control ◽  
Recent Advances ◽  
New Findings ◽  
Local Feedback ◽  
Locomotor Patterns

It is well established that locomotor patterns result from the interaction between central pattern generating networks in the nervous system, local feedback from sensory neurons about movements and forces generated in the locomotor organs, and coordinating signals from neighboring segments or appendages. This review addresses the issue of how the movements of multi-segmented locomotor organs are coordinated and provides an overview of recent advances in understanding sensory control and the internal organization of central pattern generating networks that operate multi-segmented locomotor organs, such as a walking leg. Findings from the stick insect and the cat are compared and discussed in relation to new findings on the lamprey swimming network. These findings support the notion that common schemes of sensory feedback are used for generating walking and that central neural networks controlling multi-segmented locomotor organs generally encompass multiple central pattern generating networks that correspond with the segmental structure of the locomotor organ.

Altered Gravity Highlights Central Pattern Generator Mechanisms

2008 ◽  
Vol 100 (5) ◽  
pp. 2819-2824 ◽  
Author(s):  
Olivier White ◽  
Yannick Bleyenheuft ◽  
Renaud Ronsse ◽  
Allan M. Smith ◽  
Jean-Louis Thonnard ◽  
...  
Keyword(s):  
Neural Networks ◽  
Resonant Frequency ◽  
Parabolic Flight ◽  
Central Pattern Generators ◽  
Arm Movement ◽  
Rhythmic Movements ◽  
Earth Environment ◽  
Major Interest ◽  
Microgravity Conditions ◽  
Pattern Generators

In many nonprimate species, rhythmic patterns of activity such as locomotion or respiration are generated by neural networks at the spinal level. These neural networks are called central pattern generators (CPGs). Under normal gravitational conditions, the energy efficiency and the robustness of human rhythmic movements are due to the ability of CPGs to drive the system at a pace close to its resonant frequency. This property can be compared with oscillators running at resonant frequency, for which the energy is optimally exchanged with the environment. However, the ability of the CPG to adapt the frequency of rhythmic movements to new gravitational conditions has never been studied. We show here that the frequency of a rhythmic movement of the upper limb is systematically influenced by the different gravitational conditions created in parabolic flight. The period of the arm movement is shortened with increasing gravity levels. In weightlessness, however, the period is more dependent on instructions given to the participants, suggesting a decreased influence of resonant frequency. Our results are in agreement with a computational model of a CPG coupled to a simple pendulum under the control of gravity. We demonstrate that the innate modulation of rhythmic movements by CPGs is highly flexible across gravitational contexts. This further supports the involvement of CPG mechanisms in the achievement of efficient rhythmic arm movements. Our contribution is of major interest for the study of human rhythmic activities, both in a normal Earth environment and during microgravity conditions in space.

scholarly journals An optimality principle for locomotor central pattern generators

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hansol X. Ryu ◽  
Arthur D. Kuo
Keyword(s):  
Internal Model ◽  
Sensory Feedback ◽  
Motor Pattern ◽  
Central Pattern Generators ◽  
Optimal Estimation ◽  
Legged Locomotion ◽  
Energetic Cost ◽  
Fictive Locomotion ◽  
Cost Of Transport ◽  
Pattern Generators

AbstractTwo types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re-interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with neural circuitry compatible with classic CPG models, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.

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