Neural and Computational Modeling of Movement ControlNing Lan, Vincent C. K. Cheung, Simon C. Gandevia Frontiers Media SA, Apr 17, 2017 In the study of sensorimotor systems, an important research goal has been to understand the way neural networks in the spinal cord and brain interact to control voluntary movement. Computational modeling has provided insight into the interaction between centrally generated commands, proprioceptive feedback signals and the biomechanical responses of the moving body. Research in this field is also driven by the need to improve and optimize rehabilitation after nervous system injury and to devise biomimetic methods of control in robotic devices. This research topic is focused on efforts dedicated to identify and model the neuromechanical control of movement. Neural networks in the brain and spinal cord are known to generate patterned activity that mediates coordinated activation of multiple muscles in both rhythmic and discrete movements, e.g. locomotion and reaching. Commands descending from the higher centres in the CNS modulate the activity of spinal networks, which control movement on the basis of sensory feedback of various types, including that from proprioceptive afferents. The computational models will continue to shed light on the central strategies and mechanisms of sensorimotor control and learning. This research topic demonstrated that computational modeling is playing a more and more prominent role in the studies of postural and movement control. With increasing ability to gather data from all levels of the neuromechanical sensorimotor systems, there is a compelling need for novel, creative modeling of new and existing data sets, because the more systematic means to extract knowledge and insights about neural computations of sensorimotor systems from these data is through computational modeling. While models should be based on experimental data and validated with experimental evidence, they should also be flexible to provide a conceptual framework for unifying diverse data sets, to generate new insights of neural mechanisms, to integrate new data sets into the general framework, to validate or refute hypotheses and to suggest new testable hypotheses for future experimental investigation. It is thus expected that neural and computational modeling of the sensorimotor system should create new opportunities for experimentalists and modelers to collaborate in a joint endeavor to advance our understanding of the neural mechanisms for postural and movement control. The editors would like to thank Professor Arthur Prochazka, who helped initially to set up this research topic, and all authors who contributed their articles to this research topic. Our appreciation also goes to the reviewers, who volunteered their time and effort to help achieve the goal of this research topic. We would also like to thank the staff members of editorial office of Frontiers in Computational Neuroscience for their expertise in the process of manuscript handling, publishing, and in bringing this ebook to the readers. The support from the Editor-in-Chief, Dr. Misha Tsodyks and Dr. Si Wu is crucial for this research topic to come to a successful conclusion. We are indebted to Dr. Si Li and Ms. Ting Xu, whose assistant is important for this ebook to become a reality. Finally, this work is supported in part by grants to Dr. Ning Lan from the Ministry of Science and Technology of China (2011CB013304), the Natural Science Foundation of China (No. 81271684, No. 61361160415, No. 81630050), and the Interdisciplinary Research Grant cross Engineering and Medicine by Shanghai Jiao Tong University (YG20148D09). Dr. Vincent Cheung is supported by startup funds from the Faculty of Medicine of The Chinese University of Hong Kong. Guest Associate Editors Ning Lan, Vincent Cheung, and Simon Gandevia |
Contents
Neural and Computational Modeling of Movement Control | 6 |
Minimax feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty | 9 |
Spinal circuits can accommodate interaction torques during multijoint limb movements | 23 |
Emulated muscle spindle and spiking afferents validates VLSI neuromorphic hardware as a testbed for sensorimotor function and disease | 41 |
implications for vergenceversion interactions | 50 |
Major remaining gaps in models of sensorimotor systems | 64 |
shared ideas but different functions in motor control | 75 |
Subjectspecific computational modeling of DBS in the PPTg area | 80 |
The lateral reticular nucleus integration of descending and ascending systems regulating voluntary forelimb movements | 104 |
a computational analysis | 116 |
application to feedback control | 124 |
Coordinated alpha and gamma control of muscles and spindles in movement and posture | 137 |
A Computational Model for Aperture Control in ReachtoGrasp Movement Based on Predictive Variability | 152 |
An Assessment of Six Muscle Spindle Models for Predicting Sensory Information during Human Wrist Movements | 167 |
Back cover | 179 |
Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric | 93 |