Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Neuromuscular Control Systems, Models of

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_711-1



The neuromuscular control system is the network of neurons and muscles involved in the control of movement and posture. In many instances, the definition of the system also includes the sensors, sensory processing circuitry, and/or the passive mechanical structures that influence movement. Models of neuromuscular control systems, which are mathematical representations of one or more components, are used extensively in scientific investigations of neural control and in engineering development of biomimetic systems.

Detailed Description

An animal’s ability to move is critical for exploration, interaction with the environment, and ultimately, survival. Neuromotor systems have been some of the most studied in neuroscience because movement is a readily observable behavior and the experimental environment can often be altered to manipulate the demands on the motor control system. The experimental paradigms...


Internal Model Neural Control Central Pattern Generator Motor Program Oscillatory Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.


  1. Abbas JJ (2011) Biomimetic adaptive control algorithms. In: Jung R (ed) Biohybrid systems: nerves, interfaces and machines. Wiley-VCH, WeinheimGoogle Scholar
  2. Abbas JJ, Abraham A (2014) Biomimetic approaches to physiological control. In: Bronzino JD (ed) The biomedical engineering handbook. CRC, Boca RatonGoogle Scholar
  3. Abbas JJ, Full RJ (2000) Neuromechanical interaction in cyclic movements. In: Winters JM, Crago PE (eds) Biomechanics and neural control of movement. Springer, New York, pp 177–191CrossRefGoogle Scholar
  4. Abbas JJ, Riener R (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation: J Int Neuromodulation Soc 4:187–195CrossRefGoogle Scholar
  5. Ambroise M, Levi T, Joucla S, Yvert B, Saighi S (2013) Real-time biomimetic central pattern generators in an FPGA for hybrid experiments. Front Neurosci 7:215PubMedCentralPubMedCrossRefGoogle Scholar
  6. Bar-Cohen Y (2006) Biomimetics – using nature to inspire human innovation. Bioinspir Biomim 1:P1–P12PubMedCrossRefGoogle Scholar
  7. Bicchi A, Gabiccini M, Santello M (2011) Modelling natural and artificial hands with synergies. Philos Trans R Soc Lond B Biol Sci 366:3153–3161PubMedCentralPubMedCrossRefGoogle Scholar
  8. Burdet E, Franklin DW, Milner TE (2013) Human robotics: neuromechanics and motor control. MIT Press, Cambridge, MAGoogle Scholar
  9. Cheng EJ, Loeb GE (2008) On the use of musculoskeletal models to interpret motor control strategies from performance data. J Neural Eng 5:232–253PubMedCrossRefGoogle Scholar
  10. Chiel HJ, Ting LH, Ekeberg O, Hartmann MJZ (2009) The brain in its body: motor control and sensing in a biomechanical context. J Neurosci 29:12807–12814PubMedCentralPubMedCrossRefGoogle Scholar
  11. Cook G, Stark L (1967) Derivation of a model for the human eye-positioning mechanism. Bull Math Biol 29:153–174Google Scholar
  12. Davidson PR, Wolpert DM (2005) Widespread access to predictive models in the motor system: a short review. J Neural Eng 2:S313–S319PubMedCrossRefGoogle Scholar
  13. de Rugy A, Loeb GE, Carroll TJ (2012) Muscle coordination is habitual rather than optimal. J Neurosci 32:7384–7391PubMedCrossRefGoogle Scholar
  14. de Rugy A, Loeb GE, Carroll TJ (2013) Are muscle synergies useful for neural control? Front Comput Neurosci 7:19PubMedCentralPubMedGoogle Scholar
  15. Diedrichsen J, Shadmehr R, Ivry RB (2010) The coordination of movement: optimal feedback control and beyond. Trends Cogn Sci 14:31–39PubMedCrossRefGoogle Scholar
  16. Duysens J, De Groote F, Jonkers I (2013) The flexion synergy, mother of all synergies and father of new models of gait. Front Comput Neurosci 7:14PubMedCentralPubMedCrossRefGoogle Scholar
  17. Ekeberg O (1993) A combined neuronal and mechanical model of fish swimming. Biol Cybern 69:363–374CrossRefGoogle Scholar
  18. Ermentrout GB, Terman DH (2010) Mathematical foundations of neuroscience. Springer, New YorkCrossRefGoogle Scholar
  19. Esposito MS, Capelli P, Arber S (2014) Brainstem nucleus MdV mediates skilled forelimb motor tasks. Nature 508:351–356PubMedCrossRefGoogle Scholar
  20. Franklin DW, Wolpert DM (2011) Computational mechanisms of sensorimotor control. Neuron 72:425–442PubMedCrossRefGoogle Scholar
  21. Gentner R, Edmunds T, Pai DK, d’Avella A (2013) Robustness of muscle synergies during visuomotor adaptation. Front Comput Neurosci 7:120PubMedCentralPubMedCrossRefGoogle Scholar
  22. Grillner S, Wallen P, Saitoh K, Kozlov A, Robertson B (2008) Neural bases of goal-directed locomotion in vertebrates – an overview. Brain Res Rev 57:2–12PubMedCrossRefGoogle Scholar
  23. Harris-Warrick R, Coniglio L, Barazangi N, Guckenheimer J, Gueron S (1995) Dopamine modulation of transient potassium current evokes phase shifts in a central pattern generator network. J Neurosci 15:342–358PubMedGoogle Scholar
  24. Ivanenko YP, Poppele RE, Lacquaniti F (2009) Distributed neural networks for controlling human locomotion: lessons from normal and SCI subjects. Brain Res Bull 78:13–21PubMedCrossRefGoogle Scholar
  25. Izawa J, Criscimagna-Hemminger SE, Shadmehr R (2012) Cerebellar contributions to reach adaptation and learning sensory consequences of action. J Neurosci 32:4230–4239PubMedCentralPubMedCrossRefGoogle Scholar
  26. Izhikevich EM (2010) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge, MAGoogle Scholar
  27. Jung R (2011) Biohybrid systems: nerves, interfaces and machines. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  28. Jung R, Kiemel T, Cohen AH (1996) Dynamic behavior of a neural network model of locomotor control in the lamprey. J Neurophys 75:1074–1086Google Scholar
  29. Kambara H, Shin D, Koike Y (2013) A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements. J Neurophysiol 109:2145–2160PubMedCentralPubMedCrossRefGoogle Scholar
  30. Kandel ER, Schwartz JH, Jessell TM, Siegelbaum SA, Hudspeth AJ (2013) Principles of neural science, 5th edn. McGraw Hill, New YorkGoogle Scholar
  31. Katona PG, Poitras JW, Barnett GO, Terry BS (1970) Cardiac vagal efferent activity and heart period in the carotid sinus reflex. Am J Physiol 218:115–133Google Scholar
  32. Krakauer JW, Mazzoni P (2011) Human sensorimotor learning: adaptation, skill, and beyond. Curr Opin Neurobiol 21:636–644PubMedCrossRefGoogle Scholar
  33. Loeb GE (2012) Optimal isn’t good enough. Biol Cybern 106:757–765PubMedCrossRefGoogle Scholar
  34. Marmarelis VZ (1997) Modeling methodology for nonlinear physiological systems. Ann Biomed Eng 25:239–251PubMedCrossRefGoogle Scholar
  35. Mileusnic MP, Brown IE, Lan N, Loeb GE (2006) Mathematical models of proprioceptors. I. Control and transduction in the muscle spindle. J Neurophysiol 96:1772–1788PubMedCrossRefGoogle Scholar
  36. Moberget T, Gullesen EH, Andersson S, Ivry RB, Endestad T (2014) Generalized role for the cerebellum in encoding internal models: evidence from semantic processing. J Neurosci 34:2871–2878PubMedCrossRefGoogle Scholar
  37. Morasso P, Baratto L, Spada G (1999) Internal models in the control of posture. Neural Netw 12:1173–1180PubMedCrossRefGoogle Scholar
  38. Peckham PH (2007) Smart prosthetics: exploring assistive devices for the body and mind. National Academies Press, Washington, DCGoogle Scholar
  39. Pinter IJ, van Soest AJ, Bobbert MF, Smeets JB (2012) Conclusions on motor control depend on the type of model used to represent the periphery. Biol Cybern 106:441–451PubMedCrossRefGoogle Scholar
  40. Robinson DA (1973) Models of the saccadic eye movement control system. Biol Cybern 14:71–83Google Scholar
  41. Schiff SJ (2012) Neural control engineering: the emerging intersection between control theory and neuroscience. MIT Press, Cambridge, MAGoogle Scholar
  42. Schultheiss NW, Prinz AA, Butera RJ Jr (2012) Phase response curves in neuroscience: theory, experiment, and analysis. Springer, New YorkCrossRefGoogle Scholar
  43. Scott SH (2012) The computational and neural basis of voluntary motor control and planning. Trends Cogn Sci 16:541–549PubMedCrossRefGoogle Scholar
  44. Scott SH, Norman KE (2003) Computational approaches to motor control and their potential role for interpreting motor dysfunction. Curr Opin Neurol 16:693–698PubMedCrossRefGoogle Scholar
  45. Shadmehr R, Mussa-Ivaldi FA (1994) Computational elements of the adaptive controller of the human arm. In: Cowan JD, Tesauro G, Alspector J (eds) Advances in neural information processing systems. Morgan Kaufman, San MateoGoogle Scholar
  46. Shadmehr R, Mussa-Ivaldi FA (2012) Biological learning and control: how the brain builds representations, predicts events, and makes decisions. MIT Press, Cambridge, MACrossRefGoogle Scholar
  47. Shadmehr R, Smith MA, Krakauer JW (2010) Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci 33:89–108PubMedCrossRefGoogle Scholar
  48. Shenoy KV, Sahani M, Churchland MM (2013) Cortical control of arm movements: a dynamical systems perspective. Annu Rev Neurosci 36:337–359PubMedCrossRefGoogle Scholar
  49. Tazerart S, Vinay L, Brocard F (2008) The persistent sodium current generates pacemaker activities in the central pattern generator for locomotion and regulates the locomotor rhythm. J Neurosci 28:8577–8589PubMedCrossRefGoogle Scholar
  50. Ting LH, Chvatal SA, Safavynia SA, McKay JL (2012) Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement. Int J Numer Methods Biomed Eng 28:1003–1014CrossRefGoogle Scholar
  51. Todorov E (2004) Optimality principles in sensorimotor control. Nat Neurosci 7:907–915PubMedCentralPubMedCrossRefGoogle Scholar
  52. Todorov E, Jordan MI (2002) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5:1226–1235PubMedCrossRefGoogle Scholar
  53. Van de Crommert HWAA, Mulder T, Duysens J (1998) Neural control of locomotion; part II: sensory control of the central pattern generator and its relation to treadmill training. Gait Posture 7:251–263PubMedCrossRefGoogle Scholar
  54. Verdaasdonk BW, Koopman HFJM, Helm FCT (2009) Energy efficient walking with central pattern generators: from passive dynamic walking to biologically inspired control. Biol Cybern 101:49–61PubMedCrossRefGoogle Scholar
  55. Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317–1329PubMedCrossRefGoogle Scholar
  56. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269:1880–1882PubMedCrossRefGoogle Scholar
  57. Young LR, Stark L (1963) A discrete model for eye tracking movements. IEEE Trans Mil Electron 7:113–115Google Scholar
  58. Zelik KE, Huang TW, Adamczyk PG, Kuo AD (2014) The role of series ankle elasticity in bipedal walking. J Theor Biol 346:75–85PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Biological and Health Systems EngineeringArizona State UniversityPhoenixUSA