J Comp Physiol B

DOI 10.1007/s00360-008-0289-1

REVIEW

Motor unit recruitment for dynamic tasks: current understanding and future directions

Emma F. Hodson-Tole · James M. Wakeling

Received: 1 April 2008 / Revised: 12 June 2008 / Accepted: 19 June 2008© Springer-Verlag 2008

Abstract Skeletal muscle contains many muscle Wbresthat are functionally grouped into motor units. For anymotor task there are many possible combinations of motorunits that could be recruited and it has been proposed that asimple rule, the ‘size principle’, governs the selection ofmotor units recruited for diVerent contractions. Motor unitscan be characterised by their diVerent contractile, energeticand fatigue properties and it is important that the selectionof motor units recruited for given movements allows unitswith the appropriate properties to be activated. Here wereview what is currently understood about motor unitrecruitment patterns, and assess how diVerent recruitmentpatterns are more or less appropriate for diVerent move-ment tasks. During natural movements the motor unitrecruitment patterns vary (not always holding to the sizeprinciple) and it is proposed that motor unit recruitment islikely related to the mechanical function of the muscles.Many factors such as mechanics, sensory feedback, andcentral control inXuence recruitment patterns and conse-quently an integrative approach (rather than reductionist) isrequired to understand how recruitment is controlled duringdiVerent movement tasks. Currently, the best way toachieve this is through in vivo studies that relate recruit-ment to mechanics and behaviour. Various methods fordetermining motor unit recruitment patterns are discussed,

in particular the recent wavelet-analysis approaches thathave allowed motor unit recruitment to be assessed duringnatural movements. Directions for future studies into motorrecruitment within and between functional task groups andmuscle compartments are suggested.

Keywords Neuromechanics · Electromyography · Skeletal muscle

Introduction

The majority of mammalian muscles are composed of amixture of muscle Wbre types, with a continuum of intrinsicproperties existing within each muscle (Bottinelli et al.1994a, b). The extrafusal muscle Wbres receive their nervesupply via �-motoneurons, with each �-motoneuron inner-vating a number of muscle Wbres. Force production resultsfrom a series of electrochemical processes, initiated in amuscle Wbre by the Wring of its associated �-motoneuron. Inmany studies of skeletal muscle the basic functional unit ofmuscle is considered to be the motor unit, consisting of an�-motoneuron and all the muscle Wbres it innervates (Sher-rington 1929). A given level of sub-maximal stress andstrain, generated over a speciWc time period, could beachieved by activating many diVerent combinations ofmotor units. The degrees-of-freedom within the systemsuggest that there is likely to be one or more simplifyingstrategies that ensure the correct stress and strain are gener-ated over an appropriate time period for each given motortask, without overloading the central nervous system. Iden-tifying recruitment strategies and determining their func-tional signiWcance has proved challenging. Technologicaladvancements and modern analytical techniques do, how-ever, mean that current understanding of this topic is

Communicated by I. D. Hume.

E. F. Hodson-ToleSchool of Applied Physiology, Georgia Institute of Technology, Atlanta, GE, USA

J. M. Wakeling (&)School of Kinesiology, Simon Fraser University, Burnaby, BC, Canadae-mail: wakeling@sfu.ca

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improving. Here, we provide an overview of the patterns ofmotor unit recruitment that have been observed historicallyand more recently, and methods by which patterns may beidentiWed and quantiWed.

Motor unit recruitment patterns: a review

The corner stone of current understanding of motor unitrecruitment patterns was proposed by Henneman (1957).His work on decerebrate cats indicated that, in response togreater stimulation, motoneurons were recruited in anorderly fashion from the smallest through to the largest.During derecruitment, the inverse order was seen so motorunits recruited Wrst were the last to be deactivated. Follow-ing further work, involving stretch reXexes in decerebratecats, he termed this recruitment strategy ‘the size principle’(Henneman et al. 1965a, b). Motor unit size has beenshown to vary both within and between muscles with atrend for smaller units to be composed of slower twitchmuscle Wbres and larger motor units to be composed offaster twitch muscle Wbres (McPhedran et al. 1965a, b). Inaddition, smaller motor units have smaller diameter nerveaxons, which result in slower action potential conductionvelocities along them (Bawa et al. 1984). The size principletherefore predicts that, based on both contractile propertiesand action potential conduction velocities, faster motorunits will be recruited after slower motor units have beenactivated and will be the Wrst motor units to be derecruited.This pattern is facilitated by the relationship between themotor unit �-motoneuron and its associated Ia aVerent neu-rons, from which it receives excitatory synaptic input. Thenumber of connections between the motoneuron and the IaaVerent neuron is independent of motoneuron cell size,meaning that the synaptic density per unit area variesinversely with the size of the motoneuron (Stein and Ber-toldi 1981). Smaller motoneurons therefore receive greatersynaptic inputs and will reach their depolarising thresholdbefore larger motoneurons.

Orderly recruitment of motor units has been proposed tohave several functional advantages. Firstly, it is thought tosimplify central nervous system control of muscle contrac-tions (Henneman et al. 1974). Orderly recruitment alsoensures that the slowest, most fatigue resistant, motor unitsare recruited Wrst for any given task (Henneman and Olson1965). The faster, fast fatiguing, motor units are thereforereserved for infrequent, high intensity tasks such as jump-ing, where they can provide high forces for a short periodof time. In addition, as faster motor units are composed of agreater number of muscle Wbres they are able to producegreater force than slower motor units (Milner-Brown et al.1973). Orderly recruitment, therefore, facilitates a smoothforce increment as it leads to a force increase that is roughly

proportional to the level of force at which the motor unitwas recruited (Henneman and Olson 1965; Zajac and Faden1985).

Since it was Wrst proposed a large amount of experimen-tal evidence has been produced that supports the predic-tions of the size principle (Fedde et al. 1969; Freund et al.1975; HoVer et al. 1987a; Hogrel 2003; Kier and Curtin2002; Milner-Brown et al. 1973; Tanji and Kato 1973). Agrowing number of examples, however, indicate thatorderly recruitment of motor units may not always occur.Studies in humans have shown that auditory and visualfeedback can alter recruitment orders (Basmajian 1963), ascan variations in proprioceptive inputs (Wagman et al.1965) and cutaneous stimulations (Stephens et al. 1978). Inaddition, rapid shortening of the extensor digitorum brevis(Grimby and Hannerz 1977) and rapid lengthening of thetriceps surae (Nardone et al. 1989) also result in preferen-tial recruitment of faster motor units. Non-orderly recruit-ment has also been reported from the results of glycogendepletion studies of demanding activities such as supra-maximal cycling in humans (Gollnick et al. 1974) andjumping in the bushbaby (Gillespie et al. 1974). Morerecently it has been suggested that motor units, within anindividual muscle, may form groups that can be indepen-dently activated to fulWl speciWc functional roles (HoVeret al. 1987b; Loeb 1985). These pools of motor unit havebeen termed ‘task groups’ and have been shown to be selec-tively recruited for diVerent kinematic conditions within amotor task such as a stride or a grasping movement (HoVeret al. 1987b; Loeb 1985; Riek and Bawa 1992; Von Tschar-ner and Goepfert 2006; Wakeling and Rozitis 2004). It is,therefore, likely that recruitment strategies other than thosepredicted by the size principle may be used during locomo-tion. It is possible that mechanical factors may inXuence astrategy for motor unit recruitment. In some instances it iscounterintuitive for slower motor units to be recruited forall motor tasks, speciWcally those that require shortening ata fast strain rate or have high cycle frequencies, as themechanical properties of the slower motor units suggestthey would contribute little or nothing to force developmentduring rapid movements (Rome et al. 1988). Until recentlyhowever, the diYculty has been in Wnding a reliable andsensitive measure of in vivo activation of motor unit popu-lations, and hence recruitment, that can be used to investi-gate alternate motor unit recruitment strategies duringnatural movements in intact individuals.

Determining in vivo motor unit recruitment patterns

Activity within a motor unit can be detected by measuringthe bioelectric signals that result from action potentialstravelling along the nerve axons or the muscle Wbres andthere are several methods through which this can be

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achieved. Single unit activity can be recorded from chroni-cally implanted Wne-wire microelectrodes that ‘Xoat’ in thespinal canal structures such as the dorsal root ganglia andventral roots; this procedure has been performed on freelymoving cats (HoVer et al. 1981; Loeb et al. 1977; Proc-hazka et al. 1976). The results from such studies have iden-tiWed fundamental principles of motor control, andparticularly relevant to this review was the proposal thatmotor units were recruited in task groups according to thedemands required during diVerent movements (Loeb 1985).

An alternative approach to identify single motor unitactivity is using Wne-wire or needle electrodes inserted intothe muscle (Olson et al. 1968). Motor unit action potentials(MUAPs) vary in magnitude due to the combination of anumber of factors relating to: (1) the muscles anatomy, i.e.the diameter of the muscle Wbres and the number of Wbreswithin each unit and (2) the recording technique, i.e. signalattenuation between the active Wbres and the recordingelectrodes. Additionally, the shape of the MUAP dependson the intrinsic properties of the constituent muscle Wbres(Buchthal et al. 1973), the intracellular and extracellularconcentrations of ions (that can vary for instance withfatigue; Brody et al. 1991) and the distance and orientationof the electrodes relative to the active Wbres (Lindstrom andMagnusson 1977). Individual motor units can be character-ised on the basis of the amplitude or, additionally, the shapeof the recorded MUAPs. Implanting several electrodes togive diVerent combinations of electrode orientations rela-tive to the active motor units can increase the resolution ofthese techniques, and this is the basis of quadriWlar elec-trodes (DeLuca 1993). These methods are particularly use-ful for studying isometric contractions when there is astable relationship between the electrode position and themotor units. During dynamic tasks the shape changes thatoccur within the muscles can alter the relative positioningof the electrodes and thus the characteristics of the recordedMUAPs. Furthermore, during high intensity contractionsthe interference patterns generated by MUAPs from diVer-ent motor units can change the size and shape of therecorded signal (DeLuca 1997) and this can hinder the clas-siWcation of individual units.

In some studies where individual motor units have beencharacterised on the basis of the MUAPs their Wring pat-terns have been documented relative to their Wring thresh-olds. An association exists between the Wring threshold andthe type of muscle Wbre within the motor unit (Andreassenand Arendt-Nielsen 1987; Garnett et al. 1978), and there-fore diVerent types of motor unit can be distinguished inthis way. A further way to distinguish types of motor unit isthrough the conduction velocity of their MUAPs. MUAPconduction velocity increases for faster muscle Wbres(Kupa et al. 1995; Sadoyama et al. 1988; Wakeling andSyme 2002). It is commonly thought that this is due to

diVerences in the muscle Wbre diameter (in a manner akin tonerve axons; Hodgkin 1954). However, the only evidencefor this is from in vitro preparations from the frog (Hakans-son 1956) where it would be expected that lack of physio-logical packing of the muscle Wbres would permit theconduction velocity to vary with Wbre diameter (Buchthalet al. 1955). To our knowledge there is no evidence from invivo data to support the assumption that variations in con-duction velocity are caused by muscle Wbre diameter, and incontrast large variations in Wbre diameter have been shownto exist with very little variation in conduction velocity (orthe associated mean frequency of the myoelectric signal;Wakeling and Syme 2002). In contrast, it has been sug-gested that diVerences in conduction velocity can occur dueto diVerences in the electrical membrane properties of thediVerent Wbre types (Buchthal et al. 1955) that are known tovary between muscle Wbre types (Albuquerque and ThesleV1968; LuV and Atwood 1972; Wallinga-De Jonge et al.1985). Conduction velocities can be determined for bothWne-wire (Wakeling and Syme 2002) and surface EMGrecordings (typically using arrays of electrodes mounted tothe surface of the skin (Farina et al. 2002; Hogrel 2003;Houtman et al. 2003; Sadoyama et al. 1988).

If the purpose of the measurements is to identify patternsof recruitment between populations of motor units, but theidentity of individual units is not important then a numberof further approaches can be used. In particular, the spectralproperties of recorded MUAPs can give indications as tothe Wbre types active at the time of recording. Each MUAPis conducted along the muscle Wbres at a distinct velocityand thus can be detected by the recording electrodes for acharacteristic time and this gives the major feature of thefrequency of the MUAP spectrum. In addition, the shape ofthe MUAP also contributes to the spectral properties. TheMUAP spectrum is additionally aVected by factors such asthe muscle temperature (Stalberg 1966), fatigue status(Brody et al. 1991; Mortimer et al. 1970), fascicle length(Doud and Walsh 1995), cross-talk (Farina et al. 2002),degree of synchronisation and interference between MUAPsignals (DeLuca 1997) and the volume conductor proper-ties of the myoelectric signal through the tissue (Roeleveldet al. 1997). The recorded myoelectric signal contains theconvolution of the spectral properties of the individualMUAPs and their Wring statistics. With asynchronous andstochastic Wring, as is typically associated with myoelectricsignals, there is very little inXuence of the Wring statistics inthe myoelectric spectrum. Indeed, motor unit synchronisa-tion has been shown to contribute frequency components inthe neighbourhood of the average Wring rate (15–25 Hz;DeLuca 1997) and occurs in only small bursts withfewer than 8% of Wrings (DeLuca 1993). In Wne-wire stud-ies these low frequency components are usually Wlteredout (Hodson-Tole and Wakeling 2007). In studies where

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signals have been collected using surface electrodes how-ever, these occur within the range of frequencies typicallyanalysed (10–350 Hz) and so may result in a reduction ofthe mean frequency values. However, if the spectra areassessed by their principal components (see below) thenthis already small eVect is further diminished.

Each MUAP occurs at a distinct time, leaving a charac-teristic, time-varying spectral signal. Traditionally, thespectral properties of myoelectric signals have been charac-terised by their mean or median frequency and these mea-sures have been associated with recruitment patterns anddiVerent proportions of motor unit types (Gerdle et al.1988; Kupa et al. 1995; Wretling et al. 1987). However, themean and median frequency measures consider frequencycomponents across the whole spectrum, and these mayinclude factors in addition of the types of motor unit active(Wakeling 2007). Recently, a number of time-frequencyanalysis techniques have been used to characterise the time-varying frequency spectra of myoelectric signals (Karlssonet al. 2000; von Tscharner 2000). The uncertainty principleof signal processing means that it is not possible to pre-cisely determine everything about the time and frequencycontent of a signal simultaneously (Kaiser 1994; Caludeand Stay 1995). A balance must therefore be reachedbetween the time and frequency resolution of a resolvedsignal. We have adopted a wavelet approach developed byvon Tscharner (2000) that uses a set of wavelets that havetheir time resolutions speciWcally tuned to the physiologicalresponse time of muscle twitches. Each wavelet acts as aband-pass Wlter and enables the intensity of the signal to becalculated at diVerent times within that speciWc frequencyband. The combined wavelets extract the intensities acrossthe physiological important range of frequencies (depend-ing on the recording technique used). We have shown thatthe instantaneous spectra determined from wavelet decom-position of myoelectric signals are associated with activityfrom diVerent types of muscle Wbre using a range ofapproaches: electrical stimulation to the nerve (Wakelingand Syme 2002), voluntary contractions (Wakeling andRozitis 2004) and modelling (Wakeling 2007). Waveletapproaches have shown that the spectral properties of themyoelectric signal change along the course of a stride orgait cycle (Hodson-Tole and Wakeling 2008a; von Tscharner2000; Wakeling and Rozitis 2004), and these results supportearlier reports of task-speciWc recruitment from the nerveroots in the cat (Loeb 1985).

Motor unit action potentials show substantial (two tothreefold) variation in both conduction velocity and spec-tral frequencies between fast- and slow-Wbre types (Wake-ling et al. 2001; Wakeling and Syme 2002). However, theactual variations in spectral properties that are generated bya muscle during normal behaviours may be much more sub-tle. This is due to a number of factors that include the vol-

ume conductor eVect reshaping the spectra as themyoelectric signals pass through the tissues, the hybridproperties of many motor units resulting in intermediatecontractile and presumably myoelectric properties and therecruitment of diVerent motor units resulting in a subtleblend of these hybrid Wbres being activated. In order toresolve the diVerences in spectra that occur when motorunit recruitment patterns are varied it is important to usetechniques that are powerful in resolving spectral proper-ties. Traditional measures such as the mean or median fre-quency of the EMG spectrum consider all frequencycomponents within the analysed frequency band; these maycontain unwanted elements such as signal noise, measure-ment variation or even the low frequency elements thatoccur if synchronisation of the motor unit Wring occurs.Instead, we have found that the alternative approach ofusing principal component analysis to resolve the majorfeatures of the intensity spectra provides a powerful tool todiscriminate the Wne details of spectral shape that occurwhen the motor unit recruitment patterns are varied (vonTscharner 2002; Wakeling and Rozitis 2004; Hodson-Toleand Wakeling 2007). Principal components identify themajor components of the EMG spectra and have loadingscores that correlate with spectral shifts (Wakeling andRozitis 2004). With the appropriate experimental design(Hodson-Tole and Wakeling 2007; Wakeling and Rozitis2004; Wakeling et al. 2006) the higher principal compo-nents explain the variation in motor unit recruitment andthe lower components explain smaller sources of variationand measurement noise. It has been suggested that by con-Wning the Wnal analysis to the major principal componentsthese smaller sources of variation are excluded (a processthat does not occur for mean frequency analysis) and thiscan improve the resolution of the analysis technique(Wakeling 2007).

A further development that can be adopted to resolvethe speciWc frequency bands that indicate diVerent motorunit activity is to construct wavelets that have their prop-erties matched to the major frequency components withinthe signal, and correspondingly to the signals from diVer-ent types of motor unit. These approaches involve an ini-tial wavelet analysis of the myoeletric signals, principalcomponent classiWcation of the major frequency proper-ties followed by an optimisation to tune wavelets to thosefrequencies. These approaches have been adopted byboth Von Tscharner and Goepfert (2006) and Hodson-Tole and Wakeling (2007) and can be used to directly tar-get the signals from diVerent types of motor unit withinthe analysis. It is therefore possible to study patterns ofmotor unit recruitment using a number of diVerent tech-niques, with the choice of approach often determined bythe type of motor task to be studied and the questionbeing addressed.

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In vivo patterns of motor unit recruitment: new insights

Motor unit recruitment patterns can be studied on a numberof diVerent levels, i.e. within a group of synergistic musclessuch as Xexor or extensor muscle groups acting as a unit,within a single muscle or within speciWc regions of a singlemuscle (Riek and Bawa 1992). The size principle predictsthat faster motor units will be recruited after slower motorunits have been activated and will be the Wrst motor units tobe deactivated (Henneman and Olson 1965; Hennemanet al. 1965a) and studies of single motor units have shownthat orderly recruitment of motor units occurs within eachof the hierarchical levels described above (Riek and Bawa1992). There is however, a growing body of evidence thatsuggests orderly recruitment does not adequately describemotor unit recruitment in all situations (Fedde et al. 1969;Hodson-Tole and Wakeling 2007; 2008a, b; Hogrel 2003;Milner-Brown et al. 1973; Wakeling et al. 2001; 2006;Wakeling and Rozitis 2004). Further to this, recent workquantifying patterns of motor unit recruitment in distinctgroups of Wbre type populations, indicates that not only domotor unit recruitment patterns change in response to spe-ciWc locomotor demands, but that recruitment patterns varybetween muscles that are composed of diVerent proportionsof muscle Wbre types (Hodson-Tole and Wakeling 2008a).The functional signiWcance of these diVerences is currentlyunclear and is likely to remain so until general strategiesthat govern patterns of motor unit recruitment duringdynamic tasks are identiWed. The exact nature of the otherfactors inXuencing patterns of motor unit recruitment musttherefore be investigated.

Motor unit recruitment strategies

Motor unit recruitment for sustained, low force muscle con-tractions (e.g. postural tasks, load carrying) is wellexplained by the orderly recruitment predicted by the sizeprinciple. This strategy is considered to be beneWcialbecause recruiting slower, fatigue-resistant motor units WrstsimpliWes central nervous system control (Henneman et al.1974; Zajac and Faden 1985), makes prolonged use of themost fatigue-resistant Wbres and ensures a smooth forceincrement (Henneman and Olson 1965; Zajac and Faden1985). Such a strategy, however, is not necessarily favour-able for all motor tasks. In situations where rapid forcedevelopment is required (e.g. fast starts in swimming Wsh,high speed locomotion such as supra-maximal cycling, catpaw-shake), preferential recruitment of faster motor unitswould be advantageous due to their faster activation andrelaxation rates (Burke et al. 1973) and their potential forproducing maximum mechanical power output and maxi-mum mechanical eYciency at faster strain rates (He et al.2000). Strong evidence therefore exists to suggest that

motor unit recruitment based on mechanical demand of themotor task should occur in some situations. If preferentialrecruitment of faster motor units provides a mechanicaland/or energetic advantage it would be predicted that a sig-niWcant positive association exist between the recruitmentof faster motor units and faster muscle fascicle strain ratesduring shortening. This has been shown to be true in manduring cycling (Wakeling et al. 2006) and rats running on atreadmill (Hodson-Tole and Wakeling 2008b). In bothreports the association between strain rate and recruitmentof faster motor units was strongest in muscles with a largepopulation of faster Wbre types. Previous work has shownthat orderly recruitment of motor units is harder to identifyin large, mixed Wbre type muscles, particularly whennumerous fast contracting Wbres are present (Burke et al.1973; Burke and Rymer 1976; Fleshman et al. 1981; Proskeand Waite 1976; Stephens and Stuart 1975), suggesting thatmotor unit recruitment based on mechanical demand of themotor task may be more predominant in populations offaster motor units. Further work is required to test this sug-gestion. In addition, work should also consider whether theinXuence of mechanical factors is not only aVected by Wbretype population, but also by the motor task carried out andconsideration should be made to the interaction between themuscles and the musculoskeletal system as a whole. Spe-ciWcally, the functional demands placed on the limb andindividual muscles varies across the time course of a stride(Nigg and Wakeling 2001) and is reXected by changingcorrelations between motor unit recruitment, myoelectricintensity and muscle fascicle strain rates (Hodson-Tole andWakeling 2008b). It is therefore possible that changesoccur in both functional demand and recruitment strategy.If this were found to be true it could be an indicator of thepresence of functional task groups within a muscle. Suchgroups have previously been identiWed in cat sartorius mus-cle, where they have been shown to be active during diVer-ent stride phases and to undergo diVerent strain trajectories(HoVer et al. 1987b). DiVerent task groups may be sensitiveto diVerent stimuli and therefore be recruited in response tospeciWc locomotor events. Their presence provides a path-way through which preferential recruitment of faster motorunits within a muscle could be facilitated.

In addition to the theory that motor unit recruitmentshould be associated to the mechanical demands of themotor task, it has been suggested that activation–deactiva-tion kinetics might also be inXuential (Hodson-Tole andWakeling 2008a). It is well documented that faster motorunits have faster activation and deactivation rates thanslower motor units (Burke et al. 1973). To ensure theappropriate force and/or mechanical work is produced overthe course of a motor task, such as a stride, the timing ofmuscle activation and deactivation must accommodatethese intrinsic properties. Activation–deactivation kinetics

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determined from maximal activation of in situ muscle prep-arations do not however reXect rates which may occur invivo. Activation–deactivation kinetics are inXuenced by awide range of factors such as changes in muscle fasciclestrain (Brown et al. 1999; Close 1972; Josephson andStokes 1999), motor unit Wring frequency (Roszek et al.1994) and muscle fascicle strain rates (Brown and Loeb2000). The complex relationship that undoubtedly existsbetween these intrinsic properties and the state/behaviourof the muscle suggest that this factor may be crucial indetermining muscle function and is highly likely to inXu-ence motor unit recruitment patterns. It is well acceptedthat activation, and in particular, deactivation rates need tobe well matched to the cycle frequency of a movement(Caiozzo and Baldwin 1997; Johnston 1991; Neptune andKautz 2001). How strain, strain rate, motor unit Wring fre-quency and any other relevant factor interact in vivo andmodulate activation–deactivation rates is currently not wellunderstood. Gaining an insight into in vivo activation–deactivation rates during locomotion may therefore notonly provide further insight into the intrinsic properties ofmuscles but may also improve current understanding ofmotor control and the in vivo mechanical behaviour ofdiVerent muscles. This is therefore an important avenue forfuture work.

Mechanisms determining motor unit recruitment

When identifying factors that may inXuence motor unitrecruitment patterns it is important to consider the possiblemechanisms that control these relationships. Sensory feed-back from muscles spindles, Golgi tendon organs, jointreceptors and cutaneous receptors can all potentially inXu-ence recruitment patterns. The complex interactionsbetween diVerent types of receptor, �-motoneurons and spi-nal interneurons is, however, currently not well understoodand has been shown to vary between muscles and inresponse to diVerent motor tasks (Windhorst 2007).

The size principle theory was developed following workon stretch reXexes in cats (Henneman 1957). The stretchreXex causes a stretched muscle to contract and its primarysources of input are mechanoreceptors, sensitive to musclelength changes, called muscle spindles. Muscle spindlescontain a small number of intrafusal muscle Wbres, whichare supplied by sensory nerves (Ia and II aVerent Wbres) and�-motoneurons. The correlation between motor unit axonalconduction velocity and fused tetanic tension is weak whendata are collected from large mixed Wbre muscles (Burkeet al. 1973; Burke and Rymer 1976; Fleshman et al. 1981;Proske and Waite 1976; Stephens and Stuart 1975), withthe lack of correlation most apparent when numerous fastercontracting motor units are present. This Wnding may be aresult of the weaker monosynaptic Ia aVerent feedback sup-

plied to faster motor units, compared to feedback suppliedto slower motor units (Taylor and Gottlieb 1985). In addi-tion, a reduction in stretch reXex gain has been shown dur-ing locomotion at faster velocities (running vs. walking)(Capaday and Stein 1987). Indeed, Wring of muscle spindleaVerents varies between muscles and in response to diVer-ent fascicle length changes, i.e. shortening versus lengthen-ing contractions (Loeb 1984; Murphy and Martin 1993;Prochazka 1996; Prochazka and Gorassini 1998), and isthought to inXuence muscle mechanical responses moreduring lengthening contractions (Nichols and Houk 1976).This has led some researchers to suggest that diVerentmotor tasks may require diVerent fusimotor activation pat-terns in relation to skeletomotor activation patterns, andthat this relationship maybe modulated by the direction themuscle fascicle length changes (Murphy and Martin 1993).

As the work by Henneman et al. was conducted ondecerebrate cats many pathways, which normally inXuencemotor unit recruitment, were not left intact and could there-fore not inXuence the results. One such factor would beactivity and force generation in adjacent muscles. DuringreXex contraction of the soleus and gastrocnemius muscles,reXex inhibition of the soleus muscle is mediated by stretchof the gastrocnemius (Dacko et al. 1996). Selective inhibi-tion of slower motor units have been recorded in the catsoleus muscle, but only when soleus inhibition occurred asa result of stretch of the medial gastrocnemius (SokoloVand Cope 1996). DiVerential recruitment was never demon-strated when the stretch reXex of the soleus muscle alonewas assessed (SokoloV and Cope 1996). This work demon-strates that it is important to consider the inXuence adjacentmuscles have on each other, and that to gain better under-standing of motor control it is important to consider themusculoskeletal system as a whole, dynamic system ratherthan a set of independent units as proposed in a number ofrecent reviews (Chiel and Beer 1997; Pearson et al. 2006;Rossignol et al. 2006; Frigon and Rossignol 2006; Biew-ener and Daley 2007).

In order to aid the control of diVerent motor units withina muscle it has been proposed that muscles that performmore than one kinematic type of task may be functionallydivided into task-oriented groups of motor units, or ‘taskgroups’ (Loeb 1985). The presence of such independentgroups of motor units means that an individual muscle hasthe potential to fulWl multiple functions. Each group ofmotor units could have diVerent central connections andrecruitment patterns and, it has also been suggested, mayhave intrinsic properties optimised for the performance of aspeciWc functional task (HoVer et al. 1987b). DiVerentialactivation of diVerent muscle regions that have speciWcmechanical roles has been described in a number of mus-cles in several animal species, e.g. several cat muscles(Chanaud and Macpherson 1991; Chanaud et al. 1991;

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English and Weeks 1987; HoVer et al. 1987b; Pratt andLoeb 1991), pig masseter muscle (Herring et al. 1979), birdpectoralis muscle (Dial et al. 1987), human common digitalextensor muscle (Riek and Bawa 1992), monkey Xexor dig-itorum profundus (Schieber 1993) and guinea fowl lateralgastrocnemius muscle (Higham et al. 2008). These Wndingsindicate that functional task groups do exist within muscles,and speciWcally highlights their existence in compartmenta-lised muscles. DiVerential activation of motor units is not,however, limited to such compartmentalised muscles:selective inhibition has been identiWed in the cat soleus(SokoloV and Cope 1996), and this muscle is considered toconsist of a single neuromuscular compartment (SokoloVand Cope 1996) with motor units distributed across most ofits cross-sectional area (Cope et al. 1986). When popula-tions of motor units are studied, as with the analysis ofmyoelectric signals, the presence of functional task groupsof motor units within the muscle is likely to result in diVer-ential activation of motor units being identiWed. IdentifyingdiVerential activation of motor units in such a way does notnecessarily indicate that motor unit recruitment orderwithin a particular task group will diVer from that predictedby the size principle. Indeed, orderly recruitment of motorunits within individual task groups has been identiWed fromstudies of single motor units (Riek and Bawa 1992).

One pathway that could lead to the diVerential recruit-ment of motor units is that involving Renshaw cells. Ren-shaw cells are small neurons located between motor axoncollaterals and motoneurons in the ventral spinal chord(Renshaw 1941; 1946). They mediate recurrent inhibitionof �-motoneurons (Renshaw 1941) as well as interactingwith other types of spinal neurons: �-motoneurons (Ellaway1971), Ia inhibitory interneurons (Hultborn 1972; Hultbornet al. 1971), ascending tract cells (Lindstrom and Schom-burg 1973) and other Renshaw cells (Ryall 1970). Renshawcells are therefore thought to play an important role duringlocomotion, although the exact nature of this role iscurrently poorly understood. It has been shown that therecurrent inhibitory inXuence of Renshaw cells on �-moto-neurons diVers between motor unit types, with fast-fatigu-ing units being less inhibited than fatigue-resistant units,which in turn are less inhibited than slow units (Friedmanet al. 1981). As diVerences in cell size and excitability donot explain these diVerences, it has been proposed that thenumber of Renshaw cell projections linked to �-motoneu-rons of diVerent motor unit types diVers (Friedman et al.1981). Renshaw cells themselves appear to receive moreinput from large �-motoneurons, and are stimulated less bysmall �-motoneurons (Ryall et al. 1972). It is thereforeapparent that Renshaw cells are activated by activity infaster motor units, which leads them to preferentiallyinhibit activity in slower motor units, a pathway which hasbeen shown to be active during locomotion (Pratt and

Jordan 1980). They, therefore, provide a mechanism throughwhich preferential recruitment of faster motor units couldoccur during locomotion. The selective inhibition of motorunits in the soleus muscle, demonstrated by SokoloV andCope (1996), was conducted on decerebrate cats and sodiVerential motor unit recruitment can occur just from spi-nal circuits (SokoloV and Cope 1996). However, recurrentinhibition and the activation of Renshaw cells are addition-ally controlled by descending tracts from by supraspinalcentres (for review see Katz and Pierrot-Deseilligny 1998).DiVerential recruitment of motor units can thus be inXuencedby circuits at both spinal and supraspinal levels.

Conclusion

The size principle theory of motor unit recruitment pro-vides a very robust framework with which motor unitrecruitment patterns can be predicted, which persists whenstudies of some movements are made. The anatomicalstructure of neuromuscular system shows that aVerentsfrom the spinal and brain levels can additionally modulatemotor unit activity, while motor unit task groups (deWnedon the basis of anatomy or functionality) allow discretepopulations of motor units to be diVerentially activated.Recent work has shown that diVerential activation of taskgroups does occur in response to the mechanical demandsof the motor task. This represents a new perspective of neu-romuscular control of the musculoskeletal system and hasimportant implications for those developing musculoskele-tal models, designing rehabilitation protocols and monitor-ing musculoskeletal injury and disease, as well thoseinterested in control theory of robots. Analysis of myoelec-tric signals can now resolve activity from diVerent popula-tions of motor units from signals collected using both Wne-wire and surface techniques, and provides a means bywhich further investigation of motor unit recruitment pat-terns may be pursued.

References

Albuquerque EX, ThesleV S (1968) A comparative study of membraneproperties of innervated and chronically denervated fast and slowskeletal muscles of the rat. Acta Physiol Scand 73:471–480

Andreassen S, Arendt-Nielsen L (1987) Muscle Wbre conductionvelocity in motor units of the human anterior tibial muscle: a newsize principle parameter. J Physiol 391:561–571

Basmajian JV (1963) Control and training of individual motor units.Science 141:440–441

Bawa P, Binder MD, Ruenzel P, Henneman E (1984) Recruitment or-der of motoneurons in stretch reXexes is highly correlated withtheir axonal conduction velocity. J Neurophysiol 52:410–420

Biewener AA, Daley MA (2007) Unsteady locomotion: integratingmuscle function with whole body dynamics and neuromuscularcontrol. J Exp Biol 210:2949–2960

123

J Comp Physiol B

Bottinelli R, Betto R, SchiaYno S, Reggiani C (1994a) Maximumshortening velocity and coexistence of myosin heavy chain iso-forms in single skinned fast Wbres of rat skeletal muscle. J MuscleRes Cell Motil 15:413–419

Bottinelli R, Betto R, SchiaYno S, Reggiani C (1994b) Unloadedshortening velocity and myosin heavy chain and alkali light chainisoform composition in rat skeletal muscle Wbres. J Physiol 478(Pt2):341–349

Brody LR, Pollock MT, Roy SH, De Luca CJ, Celli B (1991) pH-in-duced eVects on median frequency and conduction velocity of themyoelectric signal. J Appl Physiol 71:1878–1885

Brown IE, Cheng EJ, Loeb GE (1999) Measured and modeled proper-ties of mammalian skeletal muscle. II. The eVects of stimulus fre-quency on force-length and force-velocity relationships. J MuscleRes Cell Motil 20:627–643

Brown IE, Loeb GE (2000) Measured and modeled properties of mam-malian skeletal muscle: IV. Dynamics of activation and deactiva-tion. J Muscle Res Cell Motil 21:33–47

Buchthal F, Guld C, Rosenfalk P (1955) Innervation zone and propaga-tion velocity in human muscles. Acta Physiol Scand 35:174–190

Buchthal F, Dahl K, Rosenfalck P (1973) Rise time of the spike poten-tial in fast and slowly contracting muscle of man. Acta PhysiolScand 87:261–269

Burke RE, Rymer WZ (1976) Relative strength of synaptic input fromshort-latency pathways to motor units of deWned type in cat medi-al gastrocnemius. J Neurophysiol 39:447–458

Burke RE, Levine DN, Tsairis P, Zajac FEIII (1973) Physiologicaltypes and histochemical proWles in motor units of the cat gastroc-nemius. J Physiol 234:723–748

Calude CS, Stay MA (1995) From the Heisenberg to Godel via Chaitin.Int J Theor Phys 44:1053–1065

Capaday C, Stein RB (1987) DiVerence in the amplitude of the humansoleus H reXex during walking and running. J Physiol 392:513–522

Caiozzo VJ, Baldwin KM (1997) Determinants of work produced byskeletal muscle: potential limitations of activation and relaxation.Am J Physiol 273:C1049–C1056

Chanaud CM, Macpherson JM (1991) Functionally complex musclesof the cat hindlimb. III. DiVerential activation within biceps femo-ris during postural perturbations. Exp Brain Res 85:271–280

Chanaud CM, Pratt CA, Loeb GE (1991) Functionally complex mus-cles of the cat hindlimb. II. Mechanical and architectural heterog-enity within the biceps femoris. Exp Brain Res 85:257–270

Chiel HJ, Beer RD (1997) The brain has a body: adaptive behaviouremerges from interaction of nervous system, body and environ-ment. Trends Neurosci 20:553–557

Close RI (1972) The relations between sarcomere length and charac-teristics of isometric twitch contractions of frog sartorius muscle.J Physiol 220:745–762

Cope TC, Bodine SC, Fournier M, Edgerton VR (1986) Soleus motorunits in chronic spinal transected cats: physiological and morpho-logical alterations. J Neurophysiol 55:1202–1220

Dacko SM, SokoloV AJ, Cope TC (1996) Recruitment of triceps suraemotor units in the decerebrate cat. I. Independence of type S unitsin soleus and medial gastrocnemius muscles. J Neurophysiol75:1997–2004

DeLuca CJ (1993) Precision decomposition of EMG signals. MethodsClin Neurophysiol 4:1–28

DeLuca CJ (1997) The use of surface electromyography in biomechan-ics. J Appl Biomech 13:135–136

Dial KP, Kaplan SR, Goslow GE Jr, Jenkins FA Jr (1987) Structureand neural control of the pectoralis in pigeons: implications forXight mechanics. Anat Rec 218:284–287

Doud JR, Walsh JM (1995) Muscle fatigue and muscle length interac-tion: eVect on the EMG frequency components. ElectromyogrClin Neurophysiol 35:331–339

Ellaway PH (1971) Recurrent inhibition of fusimotor neurones exhib-iting background discharges in the decerebrate and the spinal cat.J Physiol 216:419–439

English AW, Weeks OI (1987) An anatomical and functional analysisof cat biceps femoris and semitendinosus muscles. J Morphol191:161–175

Farina D, Fosci M, Merletti R (2002) Motor unit recruitment strategiesinvestigated by surface EMG variables. J Appl Physiol 92:235–247

Fedde MR, DeWet PD, Kitchell RL (1969) Motor unit recruitment pat-tern and tonic activity in respiratory muscles of Gallus domesti-cus. J Neurophysiol 32:995–1004

Fleshman JW, Munson JB, Sypert GW, Friedman WA (1981) Rheo-base, input resistance, and motor-unit type in medial gastrocne-mius motoneurons in the cat. J Neurophysiol 46:1326–1338

Freund HJ, Budingen HJ, Dietz V (1975) Activity of single motor unitsfrom human forearm muscles during voluntary isometric contrac-tions. J Neurophysiol 38:933–946

Friedman WA, Sypert GW, Munson JB, Fleshman JW (1981) Recur-rent inhibition in type-identiWed motoneurons. J Neurophysiol46:1349–1359

Frigon A, Rossignol S (2006) Experiments and models of sensorimotorinteractions during locomotion. Biol Cybern 95:607–627

Garnett RAF, O’Donovan MJ, Stephens JA, Taylor A (1978) Motorunits organization of human medial gastrocnemius. J Physiol287:33–43

Gerdle B, Wretling ML, Henriksson-Larsen K (1988) Do the Wbre-typeproportion and the angular velocity inXuence the mean power fre-quency of the electromyogram? Acta Physiol Scand 134:341–346

Gillespie CA, Simpson DR, Edgerton VR (1974) Motor unit recruit-ment as reXected by muscle Wbre glycogen loss in a prosimian(bushbaby) after running and jumping. J Neurol Neurosurg Psy-chiatry 37:817–824

Gollnick PD, Piehl K, Saltin B (1974) Selective glycogen depletionpattern in human muscle Wbres after exercise of varying intensityand at varying pedalling rates. J Physiol 241:45–57

Grimby L, Hannerz J (1977) Firing rate and recruitment order of toeextensor motor units in diVerent modes of voluntary contraction.J Physiol 264:865–879

Hakansson CH (1956) Conduction velocity and amplitude of the actionpotential as related to circumference in the isolated Wbre of frogmuscle. Acta Physiol Scand 37:14–34

He Z-H, Bottinelli R, Pellegrino MA, Ferenczi MA, Reggiani C (2000)ATP consumption and eYciency of human single muscle Wberswith diVerent myosin isoform composition. Biophys J 79:945–961

Henneman E (1957) Relation between size of neurons and their sus-ceptibility to discharge. Science 126:1345–1347

Henneman E, Clamann HP, Gillies JD, Skinner RD (1974) Rank orderof motoneurons within a pool: law of combination. J Neurophys-iol 37:1338–1349

Henneman E, Olson CB (1965) Relations between structure and func-tion in the design of skeletal muscles. J Neurophysiol 28:581–598

Henneman E, Somjen G, Carpenter DO (1965a) Excitability and inhi-bitability of motoneurons of diVerent sizes. J Neurophysiol28:599–620

Henneman E, Somjen G, Carpenter DO (1965b) Functional signiW-cance of cell size in spinal motoneurons. J Neurophysiol 28:560–580

Herring SW, Grimm AF, Grimm BR (1979) Functional heterogeneityin a multipinnate muscle. Am J Anat 154:563–576

Higham TE, Biewener A, Wakeling JM (2008) Functional diversiWca-tion within and between muscle synergisists during locomotion.Biology Letters 4:41–44

Hodgkin AL (1954) A note on conduction velocity. J Physiol 125:221–224

123

J Comp Physiol B

Hodson-Tole EF, Wakeling JM (2007) Variations in motor unitrecruitment patterns occur within and between muscles in the run-ning rat (Rattus norvegicus). J Exp Biol 210:2333–2345

Hodson-Tole EF, Wakeling JM (2008a) Motor unit recruitment pat-terns 1: responses to changes in locomotor velocity and incline. JExp Biol 211:1882–1892

Hodson-Tole EF, Wakeling JM (2008b) Motor unit recruitment pat-terns 2: the inXuence on myoelectric intensity and muscle fasciclestrain rate. J Exp Biol 211:1893–1902

HoVer JA, Loeb GE, Marks WB, O’Donovan MJ, Pratt CA, Sugano N(1987a) Cat hindlimb motoneurons during locomotion. I. Desti-nation, axonal conduction velocity, and recruitment threshold. JNeurophysiol 57:510–529

HoVer JA, Loeb GE, Sugano N, Marks WB, O’Donovan MJ, Pratt CA(1987b) Cat hindlimb motoneurons during locomotion. III. Func-tional segregation in sartorius. J Neurophysiol 57:554–562

HoVer JA, O’Donovan MJ, Pratt CA, Loeb GE (1981) Discharge pat-terns of hindlimb motoneurons during normal cat locomotion.Science 213:466–467

Hogrel JY (2003) Use of surface EMG for studying motor unit recruit-ment during isometric linear force ramp. J Electromyogr Kinesiol13:417–423

Houtman CJ, Stegeman DF, Van Dijk JP, Zwarts MJ (2003) Changesin muscle Wber conduction velocity indicate recruitment of dis-tinct motor unit populations. J Appl Physiol 95:1045–1054

Hultborn H (1972) Convergence on interneurones in the reciprocal Iainhibitory pathway to motoneurones. Acta Physiol Scand Suppl375:1–42

Hultborn H, Jankowska E, Lindstrom S (1971) Recurrent inhibitionfrom motor axon collaterals of transmission in the Ia inhibitorypathway to motoneurones. J Physiol 215:591–612

Johnston IA (1991) Muscle action during locomotion: a comparativeperspective. J Exp Biol 160:167–185

Josephson R, Stokes D (1999) Work-dependent deactivation of a crus-tacean muscle. J Exp Biol 202:2551–2565

Kaiser G (1994) A friendly guide to wavelets. Birkhaeuser, BostonKarlsson S, Yu J, Akay M (2000) Time-frequency analysis of myo-

electric signals during dynamic contractions: a comparativestudy. IEEE Trans Biomed Eng 47:228–238

Katz R, Pierrot-Deseilligny E (1998) Recurrent inhibition in humans.Prog Neurobiol 27:325–355

Kier WM, Curtin NA (2002) Fast muscle in squid (Loligo pealei): con-tractile properties of a specialized muscle Wbre type. J Exp Biol205:1907–1916

Kupa EJ, Roy SH, Kandarian SC, De Luca CJ (1995) EVects of muscleWber type and size on EMG median frequency and conductionvelocity. J Appl Physiol 79:23–32

Lindstrom LH, Magnusson RI (1977) Interpretation of myoelectricpower spectra: a model and its applications. Proc IEEE 65:653–662

Lindstrom S, Schomburg ED (1973) Recurrent inhibition from motoraxon collaterals of ventral spinocerebellar tract neurones. ActaPhysiol Scand 88:505–515

Loeb GE (1984) The control responses of mammalian muscle spindlesduring normally executed tasks. Exerc Sport Sci Rev 12:157–204

Loeb GE (1985) Motoneurone task groups: coping with kinematic het-erogeneity. J Exp Biol 115:137–146

Loeb GE, Bak MJ, Duysens J (1977) Long-term unit recording fromsomatosensory neurons in the spinal ganglia of the freely walkingcat. Science 197:1192–1194

LuV AR, Atwood HL (1972) Membrane properties and contraction ofsingle muscle Wbers in the mouse. Am J Physiol 222:1435–1440

McPhedran AM, Wuerker RB, Henneman E (1965a) Properties of mo-tor units in a heterogeneous pale muscle (M. Gastrocnemius) ofthe cat. J Neurophysiol 28:85–99

McPhedran AM, Wuerker RB, Henneman E (1965b) Properties of mo-tor units in a homogeneous red muscle (Soleus) of the cat. J Neu-rophysiol 28:71–84

Milner-Brown HS, Stein RB, Yemm R (1973) The orderly recruitmentof human motor units during voluntary isometric contractions. JPhysiol 230:359–370

Mortimer JT, Magnusson R, Petersen I (1970) Conduction velocity inischemic muscle: eVect on EMG frequency spectrum. Am J Phys-iol 219:1324–1329

Murphy PR, Martin HA (1993) Fusimotor discharge patterns duringrhythmic movements. Trends Neurosci 16:273–278

Nardone A, Romano C, Schieppati M (1989) Selective recruitment ofhigh-threshold human motor units during voluntary isotoniclengthening of active muscles. J Physiol 409:451–471

Neptune RR, Kautz SA (2001) Muscle activation and deactivationdynamics: the governing properties in fast cyclical human move-ment performance? Exerc Sport Sci Rev 29:76–81

Nichols TR, Houk JC (1976) Improvement in linearity and regulationof stiVness that results from the actions of the stretch reXex. JNeurophysiol 29:119–142

Nigg BM, Wakeling JM (2001) Impact forces and muscle tuning: anew paradigm. Exerc Sport Sci Rev 29:37–41

Olson CB, Carpenter DO, Henneman E (1968) Orderly recruitment ofmuscle action potentials. Arch Neurol 19:591–597

Pearson K, Ekeberg Ö, Büschges A (2006) Assessing sensory functionin locomotor systems using neuro-mechanical simulations.Trends Neurosci 29:625–631

Pratt CA, Jordan LM (1980) Recurrent inhibition of motoneurons indecerebrate cats during controlled treadmill locomotion. J Neuro-physiol 44:489–500

Pratt CA, Loeb GE (1991) Functionally complex muscles of the cathindlimb. I. Patterns of activation across sartorius. Exp Brain Res85:243–256

Prochazka A (1996) Proprioceptive feedback and movement regula-tion. In: Rowell L, Shepherd JT (eds) Handbook of physiology,exercise: regulation and integration of multiple systems. Ameri-can Physiological Society, New York, pp 89–127

Prochazka A, Gorassini M (1998) Ensemble Wring of muscle aVerents re-corded during normal locomotion in cats. J Physiol 507:293–304

Prochazka A, Westerman RA, Ziccone SP (1976) Discharges of singlehindlimb aVerents in the freely moving cat. J Neurophysiol39:1090–1104

Proske U, Waite PM (1976) The relation between tension and axonalconduction velocity for motor units in the medial gastrocnemiusmuscle of the cat. Exp Brain Res 26:325–328

Renshaw B (1941) InXuence of discharge of motoneurons upon exci-tation of neighboring motoneurons. J Neurophysiol 4:167–183

Renshaw B (1946) Central eVects of centripetal impulses in axons ofspinal ventral roots. J Neurophysiol 9:191–204

Riek S, Bawa P (1992) Recruitment of motor units in human forearmextensors. J Neurophysiol 68:100–108

Rossignol S, Dubuc R, Gossard JP (2006) Dynamic sensorimotorinteractions in locomotion. Physiol Rev 86:89–154

Roeleveld K, Blok JH, Stegeman DF, van Oosterom A (1997) Volumeconduction models for surface EMG; confrontation with mea-surements. J Electromyogr Kinesiol 7:221–232

Rome LC, Funke RP, Alexander RM, Lutz GJ, Aldridge H, Scott F,Freadman M (1988) Why animals have diVerent muscle Wbretypes. Nature 335:824–827

Roszek B, Baan GC, Huijing PA (1994) Decreasing stimulation fre-quency-dependent length-force characteristics of rat muscle. JAppl Physiol 77:2115–2124

Ryall RW (1970) Renshaw cell mediated inhibition of Renshaw cells:patterns of excitation and inhibition from impulses in motor axoncollaterals. J Neurophysiol 33:257–270

123

J Comp Physiol B

Ryall RW, Piercey MF, Polosa C, Goldfarb J (1972) Excitation of Ren-shaw cells in relation to orthodromic and antidromic excitation ofmotoneurons. J Neurophysiol 35:137–148

Sadoyama T, Masuda T, Miyata H, Katsuta S (1988) Fibre conductionvelocity and Wbre composition in human vastus lateralis. Eur JAppl Physiol Occup Physiol 57:767–771

Schieber MH (1993) Electromyographic evidence of two functionalsubdivisions in the rhesus monkey’s Xexor digitorum profundus.Exp Brain Res 95:251–260

Sherrington C (1929) Ferrier lecture: some functional problems attach-ing to convergence. Proc R Soc B 105:332–362

SokoloV AJ, Cope TC (1996) Recruitment of triceps surae motor unitsin the decerebrate cat. II. Heterogeneity among soleus motorunits. J Neurophysiol 75:2005–2016

Stalberg E (1966) Propagation velocity in human muscle Wbers in situ.Acta Physiol Scand Suppl 287:1–112

Stein RB, Bertoldi R (1981) Unique contributions of slow and fastextensor muscles to the control of limb movements. In: DesmedtJE (ed) Motor unit types, recruitment and plasticity in health anddisease. Karger, Basel, pp 85–96

Stephens JA, Stuart DG (1975) The motor units of cat medial gastroc-nemius: speed–size relations and their signiWcance for the recruit-ment order of mortor units. Brain Res 91:177–195

Stephens JA, Garnett R, Buller NP (1978) Reversal of recruitment or-der of single motor units produced by cutaneous stimulation dur-ing voluntary muscle contraction in man. Nature 272:362–364

Tanji J, Kato M (1973) Recruitment of motor units in voluntary con-traction of a Wnger muscle in man. Exp Neurol 40:759–770

Taylor A, Gottlieb S (1985) Convergence of several sensory modalitiesin motor control. In: Barnes WJP, Gladden MH (eds) Feedbackand motor control in invertebrates and vertebrates. Croon Helm,London, pp 77–91

von Tscharner V (2000) Intensity analysis in time-frequency space ofsurface myoelectric signals by wavelets of speciWed resolution. JElectromyogr Kinesiol 10:433–445

von Tscharner V (2002) Time-frequency and principal-component meth-ods for the analysis of EMGs recorded during a mildly fatiguingexercise on a cycle ergometer. J Electromyogr Kinesiol 12:479–492

Von Tscharner V, Goepfert B (2006) Estimation of the interplay be-tween groups of fast and slow muscle Wbers of the tibialis anteriorand gastrocnemius muscle while running. J Electromyogr Kinesi-ol 16:188–197

Wagman IH, Pierce DS, Burger RE (1965) Proprioceptive inXuence, involitional control of individual motor units. Nature 207:957–958

Wakeling JM (2007) Patterns of motor recruitment can be determinedusing surface EMG. J Electromyogr Kinesiol. doi:10.1016/j.jele-kin.207.09.006

Wakeling JM, Rozitis AI (2004) Spectral properties of myoelectric sig-nals from diVerent motor units in the leg extensor muscles. J ExpBiol 207:2519–2528

Wakeling JM, Syme DA (2002) Wave properties of action potentialsfrom fast and slow motor units of rats. Muscle Nerve 26:659–668

Wakeling JM, Pascual SA, Nigg BM, Von Tscharner V (2001) SurfaceEMG shows distinct populations of muscle activity when mea-sured during sustained sub-maximal exercise. Eur J Appl Physiol86:40–47

Wakeling JM, Uehli K, Rozitis AI (2006) Muscle Wbre recruitment canrespond to the mechanics of the muscle contraction. J R Soc Inter-face 3:533–544

Wallinga-De Jonge W, Gielen FL, Wirtz P, De Jong P, Broenink J(1985) The diVerent intracellular action potentials of fast and slowmuscle Wbres. Electroencephalogr Clin Neurophysiol 60:539–547

Windhorst U (2007) Muscle proprioceptive feedback and spinal net-works. Brain Res Bull 73:155–202

Wretling ML, Gerdle B, Henriksson-Larsen K (1987) EMG: a non-invasive method for determination of Wbre type proportion. ActaPhysiol Scand 131:627–628

Zajac FE, Faden JS (1985) Relationship among recruitment order, axonalconduction velocity and muscle-unit properties of type-identiWedmotor units in cat plantaris muscle. J Neurophysiol 53:1303–1322

123