On peripheral control mechanisms acting on the central pattern generators for swimming in the dogfish

1982 ◽  
Vol 98 (1) ◽  
pp. 1-22 ◽  
Author(s):  
S. Grillner ◽  
P. Wallen
Keyword(s):  
Vertebral Column ◽  
Central Pattern Generators ◽  
Control Mechanisms ◽  
Fictive Locomotion ◽  
Tail Region ◽  
Stretch Receptors ◽  
Burst Pattern ◽  
Ventral Roots ◽  
Pattern Generators ◽  
Low Amplitude

When sinusoidal movements were artificially imposed on the tail region of the curarized spinal dogfish during “fictive locomotion' the coordinated burst pattern recorded in the ventral roots was effectively entrained to follow movement frequencies above as well as below the resting rate. The entrainment was characterized by: (1) a broad range of effective movement frequencies and amplitudes (down to a few degrees); (2) frequency-dependent timing of entrained bursts to the movement; (3) constant burst durations at low and moderate frequencies; (4) incomplete entrainment in response to high or low movement frequencies combined with a low amplitude; (5) entrainment was still present when mean position of movement was displaced laterally; (6) effects persisted when the tail region was devoid of skin and muscle tissue. Entrainment effects may be explained by the activation of stretch receptors on either side of the vertebral column-spinal cord, exciting the presumed central pattern generators (CPGs) in the hemisegments ipsilateral to the stretch, while inhibiting the contralateral CPGs.

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.

A neuromechanical model explaining forward and backward stepping in the stick insect

2012 ◽  
Vol 107 (12) ◽  
pp. 3267-3280 ◽  
Author(s):  
T. I. Tóth ◽  
S. Knops ◽  
S. Daun-Gruhn
Keyword(s):  
Neuronal Networks ◽  
Central Pattern Generators ◽  
Stick Insect ◽  
Control Mechanisms ◽  
Mechanical Parts ◽  
Antagonistic Muscle ◽  
Neuromuscular Systems ◽  
Muscle Pair ◽  
Motoneuron Activity ◽  
Pattern Generators

The mechanism underlying the generation of stepping has been the object of intensive studies. Stepping involves the coordinated movement of different leg joints and is, in the case of insects, produced by antagonistic muscle pairs. In the stick insect, the coordinated actions of three such antagonistic muscle pairs produce leg movements and determine the stepping pattern of the limb. The activity of the muscles is controlled by the nervous system as a whole and more specifically by local neuronal networks for each muscle pair. While many basic properties of these control mechanisms have been uncovered, some important details of their interactions in various physiological conditions have so far remained unknown. In this study, we present a neuromechanical model of the coupled protractor-retractor and levator-depressor neuromuscular systems and use it to elucidate details of their coordinated actions during forward and backward walking. The switch from protraction to retraction is evoked at a critical angle of the femur during downward movement. This angle represents a sensory input that integrates load, motion, and ground contact. Using the model, we can make detailed suggestions as to how rhythmic stepping might be generated by the central pattern generators of the local neuronal networks, how this activity might be transmitted to the corresponding motoneurons, and how the latter might control the activity of the related muscles. The entirety of these processes yields the coordinated interaction between neuronal and mechanical parts of the system. Moreover, we put forward a mechanism by which motoneuron activity could be modified by a premotor network and suggest that this mechanism might serve as a basis for fast adaptive behavior, like switches between forward and backward stepping, which occur, for example, during curve walking, and especially sharp turning, of insects.

scholarly journals An optimality principle for locomotor central pattern generators

2019 ◽  
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 classic neural half-center circuitry, 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.New and NoteworthySensory feedback modulates the central pattern generator (CPG) rhythm in locomotion, but there lacks an explanation for how much feedback is appropriate. We propose destabilizing noise as a determinant, where an uncertain environment demands more feedback, but noisy sensors demand less. We reinterpret the CPG as an internal model for predicting body state despite noise. Optimizing its feedback yields robust and economical gait in a walking model, and explains the advantages of feedback-driven CPG control.

scholarly journals Data-driven analysis of motor activity implicates 5-HT2A neurons in backward locomotion of larval Drosophila

2018 ◽  
Author(s):  
Jeonghyuk Park ◽  
Shu Kondo ◽  
Hiromu Tanimoto ◽  
Hiroshi Kohsaka ◽  
Akinao Nose
Keyword(s):  
Neural Circuits ◽  
Central Pattern Generators ◽  
Data Driven ◽  
Neural Dynamics ◽  
Neural Population ◽  
Fictive Locomotion ◽  
Motor Patterns ◽  
Light Stimuli ◽  
Pattern Generators ◽  
Imaging Conditions

ABSTRACTRhythmic animal behaviors are regulated in part by neural circuits called the central pattern generators (CPGs). Classifying neural population activities correlated with body movements and identifying the associated component neurons are critical steps in understanding CPGs. Previous methods that classify neural dynamics obtained by dimension reduction algorithms often require manual optimization which could be laborious and preparation-specific. Here, we present a simpler and more flexible method that is based on the pre-trained convolutional neural network model VGG-16 and unsupervised learning, and successfully classifies the fictive motor patterns in Drosophila larvae under various imaging conditions. We also used voxel-wise correlation mapping to identify neurons associated with motor patterns. By applying these methods to neurons targeted by 5-HT2A-GAL4, which we generated by the CRISPR/Cas9-system, we identified two classes of interneurons, termed Seta and Leta, which are specifically active during backward but not forward fictive locomotion. Optogenetic activation of Seta and Leta neurons increased backward locomotion. Conversely, thermogenetic inhibition of 5-HT2A-GAL4 neurons or application of a 5-HT2 antagonist decreased backward locomotion induced by noxious light stimuli. This study establishes an accelerated pipeline for activity profiling and cell identification in larval Drosophila and implicates the serotonergic system in the modulation of backward locomotion.

scholarly journals Speed dependency in α-motoneuron activity and locomotor modules in human locomotion: indirect evidence for phylogenetically conserved spinal circuits

2017 ◽  
Vol 284 (1851) ◽  
pp. 20170290 ◽  
Author(s):  
Hikaru Yokoyama ◽  
Tetsuya Ogawa ◽  
Masahiro Shinya ◽  
Noritaka Kawashima ◽  
Kimitaka Nakazawa
Keyword(s):  
High Speed ◽  
Neural Control ◽  
Activity Patterns ◽  
Central Pattern Generators ◽  
Indirect Evidence ◽  
Speed Control ◽  
Human Locomotion ◽  
Control Mechanisms ◽  
Activation Patterns ◽  
Pattern Generators

Coordinated locomotor muscle activity is generated by the spinal central pattern generators (CPGs). Vertebrate studies have demonstrated the following two characteristics of the speed control mechanisms of the spinal CPGs: (i) rostral segment activation is indispensable for achieving high-speed locomotion; and (ii) specific combinations between spinal interneuronal modules and motoneuron (MN) pools are sequentially activated with increasing speed. Here, to investigate whether similar control mechanisms exist in humans, we examined spinal neural activity during varied-speed locomotion by mapping the distribution of MN activity in the spinal cord and extracting locomotor modules, which generate basic MN activation patterns. The MN activation patterns and the locomotor modules were analysed from multi-muscle electromyographic recordings. The reconstructed MN activity patterns were divided into the following three patterns depending on the speed of locomotion: slow walking, fast walking and running. During these three activation patterns, the proportion of the activity in rostral segments to that in caudal segments increased as locomotion speed increased. Additionally, the different MN activation patterns were generated by distinct combinations of locomotor modules. These results are consistent with the speed control mechanisms observed in vertebrates, suggesting phylogenetically conserved spinal mechanisms of neural control of locomotion.

Effect of Central Pattern Generators Depended Different Walking Strategies on Gait Ability of Patients after Stroke

2017 ◽  
Vol 27 (2) ◽  
pp. 40
Author(s):  
Hua WU ◽  
Zaihua RU ◽  
Congying XU ◽  
Xudong GU ◽  
Jianming FU
Keyword(s):  
Central Pattern Generators ◽  
Pattern Generators

Rhythmic Pattern Generation in Invertebrates

2018 ◽  
pp. 390-400
Author(s):  
Astrid A. Prinz
Keyword(s):  
Central Pattern Generators ◽  
Pattern Generation ◽  
Rhythmic Pattern ◽  
Neuronal Circuit ◽  
Neuronal Circuits ◽  
Timing Circuit ◽  
Core Components ◽  
Pattern Generators

This chapter begins by defining central pattern generators (CPGs) and proceeds to focus on one of their core components, the timing circuit. After arguing why invertebrate CPGs are particularly useful for the study of neuronal circuit operation in general, the bulk of the chapter then describes basic mechanisms of CPG operation at the cellular, synaptic, and network levels, and how different CPGs combine these mechanisms in various ways. Finally, the chapter takes a semihistorical perspective to discuss whether or not the study of invertebrate CPGs has seen its prime and what it has contributed—and may continue to offer—to a wider understanding of neuronal circuits in general.

Models of central pattern generators for quadruped locomotion

2001 ◽  
Vol 42 (4) ◽  
pp. 291-326 ◽  
Author(s):  
Pietro-Luciano Buono ◽  
Martin Golubitsky
Keyword(s):  
Central Pattern Generators ◽  
Quadruped Locomotion ◽  
Pattern Generators
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