Skip to main content
SpringerLink
Log in

Neural representation of muscle dynamics in voluntary movement control

  • Research Article
  • Published:
Experimental Brain Research Aims and scope Submit manuscript

Abstract

Several theories of motor control posit that the nervous system has access to a neural representation of muscle dynamics. Yet, this has not been tested experimentally. Should such a representation exist, it was hypothesized that subjects who learned to control a virtual limb using virtual muscles would improve performance faster and show greater generalization than those who learned with a less dynamically complex virtual force generator. Healthy adults practiced using their biceps brachii activity to move a myoelectrically controlled virtual limb from rest to a standard target position with maximum speed and accuracy. Throughout practice, generalization was assessed with untrained target trials and sensitivity to actuator dynamics was probed by unexpected actuator model switches. In a muscle model subject group (n = 10), the biceps electromyographic signal activated a virtual muscle that pulled on the virtual limb with a force governed by muscle dynamics, defined by a nonlinear force–length–velocity relation and series elastic stiffness. A force generator group (n = 10) performed the same task, but the actuation force was a linear function of the biceps activation signal. Both groups made significant errors with unexpected actuator dynamics switches, supporting task sensitivity to actuator dynamics. The muscle model group improved performance as fast as the force generator group and showed greater generalization in early practice, despite using an actuator with more complex dynamics. These results are consistent with a preexisting neural representation of muscle dynamics, which may have offset any learning challenges associated with the more dynamically complex virtual muscle model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  • An K, Ueba Y, Chao E, Cooney W, Linscheid R (1983) Tendon excursion and moment arm of index finger muscles. J Biomech 16:419–425

    Article  CAS  PubMed  Google Scholar 

  • Armstrong B, de Wit CC (1996) Friction modeling and compensation. In: The control handbook, vol 77, pp 1369–1382

  • Asmussen E, Bonde-Petersen F (2008) Storage of elastic energy in skeletal muscles in man. Acta Physiol Scand 91:385–392

    Article  Google Scholar 

  • Atkeson CG (1989) Learning arm kinematics and dynamics. Ann Rev Neurosci 12:157–183

    Article  CAS  PubMed  Google Scholar 

  • Bahler AS (1967) Series elastic component of mammalian skeletal muscle. Am J Physiol 213:1560–1564

    CAS  PubMed  Google Scholar 

  • Brainard DH (1997) The psychophysics toolbox. Spat Vis 10:433–436

    Article  CAS  PubMed  Google Scholar 

  • Brown IE, Loeb GE (1999) A reductionist approach to creating and using neuromusculoskeletal models. In: Winters J, Crago P (eds) Biomechanics and neural control of movement. Springer-Verlag, New York, pp 148–163

  • Caithness G, Osu R, Bays P et al (2004) Failure to consolidate the consolidation theory of learning for sensorimotor adaptation tasks. J Neurosci 24:8662–8671

    Article  CAS  PubMed  Google Scholar 

  • Cavagna GA (1977) Storage and utilization of elastic energy in skeletal muscle. Exerc Sport Sci Rev 5:89–130

    CAS  PubMed  Google Scholar 

  • Cheng EJ, Loeb GE (2008) On the use of musculoskeletal models to interpret motor control strategies from performance data. J Neural Eng 5:232

    Article  PubMed  Google Scholar 

  • Conditt MA, Gandolfo F, Mussa-Ivaldi FA (1997) The motor system does not learn the dynamics of the arm by rote memorization of past experience. J Neurophysiol 78:554–560

    CAS  PubMed  Google Scholar 

  • de Rugy A, Loeb GE, Carroll TJ (2012) Muscle coordination is habitual rather than optimal. J Neurosci 32:7384–7391

    Article  PubMed  Google Scholar 

  • Dingwell JB, Mah CD, Mussa-Ivaldi FA (2002) Manipulating objects with internal degrees of freedom: evidence for model-based control. J Neurophysiol 88:222–235

    PubMed  Google Scholar 

  • Donchin O, Francis JT, Shadmehr R (2003) Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control. J Neurosci 23:9032–9045

    CAS  PubMed  Google Scholar 

  • Edman K, Elzinga G, Noble M (1978) Enhancement of mechanical performance by stretch during tetanic contractions of vertebrate skeletal muscle fibres. J Physiol 281:139–155

    CAS  PubMed Central  PubMed  Google Scholar 

  • Edman K, Caputo C, Lou F (1993) Depression of tetanic force induced by loaded shortening of frog muscle fibres. J Physiol 466:535–552

    CAS  PubMed Central  PubMed  Google Scholar 

  • Erdemir A, McLean S, Herzog W, van den Bogert AJ (2007) Model-based estimation of muscle forces exerted during movements. Clin Biomech 22:131–154

    Article  Google Scholar 

  • Feldman AG (1966) Functional tuning of the nervous system with control of movement or maintenance of a steady posture, II: controllable parameters of the muscles. Biophysics 11:565–578

    Google Scholar 

  • Fellows S, Rack P (1987) Changes in the length of the human biceps brachii muscle during elbow movements. J Physiol 383:405–412

    CAS  PubMed Central  PubMed  Google Scholar 

  • Flanagan JR, Wing AM (1997) The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads. J Neurosci 17:1519–1528

    CAS  PubMed  Google Scholar 

  • Gandolfo F, Mussa-Ivaldi F, Bizzi E (1996) Motor learning by field approximation. Proc Natl Acad Sci 93:3843–3846

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Gerritsen KG, van den Bogert AJ, Hulliger M, Zernicke RF (1998) Intrinsic muscle properties facilitate locomotor control—a computer simulation study. Mot Control 2:206–220

    CAS  Google Scholar 

  • Gordon KE, Ferris DP (2004) Proportional myoelectric control of a virtual object to investigate human efferent control. Exp Brain Res 159:478–486

    Article  PubMed  Google Scholar 

  • Gordon A, Huxley AF, Julian F (1966) The variation in isometric tension with sarcomere length in vertebrate muscle fibres. J Physiol 184:170–192

    CAS  PubMed Central  PubMed  Google Scholar 

  • Gordon AM, Forssberg H, Iwasaki N (1994) Formation and lateralization of internal representations underlying motor commands during precision grip. Neuropsychologia 32:555–568

    Article  CAS  PubMed  Google Scholar 

  • Hall E, Flament D, Fraser C, Lemon R (1990) Non-invasive brain stimulation reveals reorganised cortical outputs in amputees. Neurosci Lett 116:379–386

    Article  CAS  PubMed  Google Scholar 

  • Hasson CJ, Caldwell GE (2012) Effects of age on mechanical properties of dorsiflexor and plantarflexor muscles. Ann Biomed Eng 40:1088–1101

    Article  PubMed  Google Scholar 

  • Hasson CJ, Shen T, Sternad D (2012) Energy margins in dynamic object manipulation. J Neurophysiol 108:1349–1365

    Article  PubMed Central  PubMed  Google Scholar 

  • Hatze H (1975) A new method for the simultaneous measurement of the moment of inertia, the damping coefficient and the location of the centre of mass of a body segmentin situ. Eur J Appl Physiol Occup Physiol 34:217–226

    Article  CAS  PubMed  Google Scholar 

  • Hill A (1938) The heat of shortening and the dynamic constants of muscle. Proc R Soc B 126:136–195

    Article  Google Scholar 

  • Hof A (2003) Muscle mechanics and neuromuscular control. J Biomech 36:1031–1038

    Article  CAS  PubMed  Google Scholar 

  • Hogan N (1984) Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans Autom Control 29:681–690

    Article  Google Scholar 

  • Hooper SL, Weaver AL (2000) Motor neuron activity is often insufficient to predict motor response. Curr Opin Neurobiol 10:676–682

    Article  CAS  PubMed  Google Scholar 

  • Jordan MI, Rumelhart DE (1992) Forward models: supervised learning with a distal teacher. Cogn Sci 16:307–354

    Article  Google Scholar 

  • Kawato M (1990) Feedback-error-learning neural network for supervised motor learning. In: Eckmiller R (ed) Advanced neural computers. North-Holland, Amsterdam, pp 365–372

    Google Scholar 

  • Kistemaker DA, Van Soest AJK, Wong JD, Kurtzer I, Gribble PL (2013) Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback. J Neurophysiol 109:1126–1139

    Article  PubMed Central  PubMed  Google Scholar 

  • Koo TK, Mak AF (2005) Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow. J Electromyogr Kinesiol 15:12–26

    Article  PubMed  Google Scholar 

  • Kubow T, Full R (1999) The role of the mechanical system in control: a hypothesis of self-stabilization in hexapedal runners. Philos Trans R Soc B 354:849–861

    Article  Google Scholar 

  • Kuiken T, Dumanian G, Lipschutz R, Miller L, Stubblefield K (2004) The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee. J Prosthet Orthot Int 28:245–253

    CAS  Google Scholar 

  • Kurtzer IL, Pruszynski JA, Scott SH (2008) Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Curr Biol 18:449–453

    Article  CAS  PubMed  Google Scholar 

  • Manal K, Gonzalez RV, Lloyd DG, Buchanan TS (2002) A real-time EMG-driven virtual arm. Comp Biol Med 32:25–36

    Article  Google Scholar 

  • Miall R, Weir D, Wolpert D, Stein J (1993) Is the cerebellum a Smith predictor? J Mot Behav 25:203–216

    Article  CAS  PubMed  Google Scholar 

  • Morton SM, Lang CE, Bastian AJ (2001) Inter- and intra-limb generalization of adaptation during catching. Exp Brain Res 141:438–445

    Article  CAS  PubMed  Google Scholar 

  • Narain D, Mamassian P, Brenner E, Smeets J, van Beers R (2013) The acquisition of hidden models in sensorimotor learning. In: 23rd annual meeting on the neural control of movement, San Juan, Puerto Rico, 16-20 April 2013

  • Ostry DJ, Feldman AG (2003) A critical evaluation of the force control hypothesis in motor control. Exp Brain Res 153:275–288

    Article  PubMed  Google Scholar 

  • Potvin J (1997) Effects of muscle kinematics on surface EMG amplitude and frequency during fatiguing dynamic contractions. J Appl Physiol 82:144–151

    CAS  PubMed  Google Scholar 

  • Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, Cambridge

    Google Scholar 

  • Radhakrishnan SM, Baker SN, Jackson A (2008) Learning a novel myoelectric-controlled interface task. J Neurophysiol 100:2397–2408

    Article  PubMed Central  PubMed  Google Scholar 

  • Reis J, Schambra HM, Cohen LG et al (2009) Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc Natl Acad Sci 106:1590–1595

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sainburg R, Ghez C, Kalakanis D (1999) Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms. J Neurophysiol 81:1045–1056

    CAS  PubMed  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208–3224

    CAS  PubMed  Google Scholar 

  • Shmuelof L, Huang VS, Haith AM, Delnicki RJ, Mazzoni P, Krakauer JW (2012) Overcoming motor “forgetting” through reinforcement of learned actions. J Neurosci 32:14617–14621a

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Smith MA, Ghazizadeh A, Shadmehr R (2006) Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol 4:e179

    Article  PubMed Central  PubMed  Google Scholar 

  • Spector SA, Gardiner PF, Zernicke RF, Roy RR, Edgerton V (1980) Muscle architecture and force–velocity characteristics of cat soleus and medial gastrocnemius: implications for motor control. J Neurophysiol 44:951–960

    CAS  PubMed  Google Scholar 

  • Svinin M, Goncharenko I, Luo Z-W, Hosoe S (2006) Reaching movements in dynamic environments: how do we move flexible objects? IEEE Trans Robot 22:724–739

    Article  Google Scholar 

  • Todorov E, Jordan MI (2002a) A minimal intervention principle for coordinated movement. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems, vol 15. MIT, Cambridge, MA, pp 27–34

  • Todorov E, Jordan MI (2002b) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5:1226–1235

    Article  CAS  PubMed  Google Scholar 

  • Winters JM, Stark L (1988) Estimated mechanical properties of synergistic muscles involved in movements of a variety of human joints. J Biomech 21:1027–1041

    Article  CAS  PubMed  Google Scholar 

  • Wolpert DM, Ghahramani Z (2000) Computational principles of movement neuroscience. Nat Neurosci 3:1212–1217

    Article  CAS  PubMed  Google Scholar 

  • Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317–1329

    Article  CAS  PubMed  Google Scholar 

  • Zajac FE (1989) Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng 17:359–411

    CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Thanks to Annalisa Hamel-Smith and Conor Bray for their assistance with data collection, and Sheng-Che Yen and anonymous reviewers for critical and insightful comments.

Author information

Authors and Affiliations

  1. Department of Physical Therapy, Movement and Rehabilitation Sciences, Neuromotor Systems Laboratory, Northeastern University, 360 Huntington Avenue, 301 Robinson Hall, Boston, MA, 02115-5005, USA

    Christopher J. Hasson

Authors
  1. Christopher J. Hasson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher J. Hasson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hasson, C.J. Neural representation of muscle dynamics in voluntary movement control. Exp Brain Res 232, 2105–2119 (2014). https://doi.org/10.1007/s00221-014-3901-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00221-014-3901-5

Keywords

Search

Navigation