Generating variability from motor primitives during infant locomotor development

Human babies start to walk on their own when they are about one year old, but before that, they can move their legs to produce movements called ‘stepping’, where they take steps when held over a surface; and kicking, where they kick in the air when lying on their backs. These two behaviors are known as ‘locomotor precursors’ and can be observed from birth.

Previous studies suggest that infants produce these movements by activating a small number of motor primitives, different modules in the nervous system – each activating a combination of muscles to produce a movement. However, babies and toddlers exhibit a lot of variability when they move, which is a hallmark of typical development that furthers exploring and learning. So far, it has been unclear whether such differences arise as soon as babies are born and if so, how a small number of motor primitives could result in this variability.

Hinnekens et al. hypothesized that the great variety of movements in infants can be generated from a small set of motor primitives, when several cycles of flexing and extending the legs are considered. To test their hypothesis, the researchers first needed to establish how and when infants generate this variability of movement. To do so, they used electromyography to record the leg muscle activity of 18 babies during either movement resulting in a body displacement (locomotor movement) or rhythmic movement. These measurements were taken at either two or three timepoints between birth and the onset of walking.

Next, the scientists used a state-of-the-art machine learning approach to model the neural basis underlying these recordings, which showed that newborns generate a lot of movement variability, but they do so by activating a small number of motor primitives, which they can combine in different ways. Hinnekens et al. also show that as babies get older, the number of motor primitives increases while the variety of movements decreases due to a more steady activation of each motor primitive.

Cerebral plasticity is maximal during the first year of life, and infants can regularly learn new motor skills, each leading to the ability to perform more movements. Motor variability is believed to play an important role in this learning process and is known to be decreased in atypical development. As such, examining motor variability may be a promising tool to identify neurodevelopmental delays at younger ages.

Variability arises at several levels of the motor system during early locomotor development. Firstly, as soon as birth, infants are able to perform a wide range of behaviors involving flexion and extension cycles of the lower limbs, such as stepping, kicking, swimming, or crawling (Forma et al., 2019; McGraw, 1941; McGraw, 1939; Sylos-Labini et al., 2020; Thelen et al., 1983b; Thelen and Fisher, 1982).Secondly, a given behavior can be realized with numerous coordination modes. For example, neonatal stepping can involve alternated steps, parallel steps, serial steps, or single steps (Siekerman et al., 2015). Similarly, toddlers can follow curved paths when walking or generate a variety of coordination patterns on the fly when cruising over varying distances (Ossmy and Adolph, 2020). Thirdly, a given coordination mode can be realized by different combinations of muscles. For example, infants demonstrate a high variability of muscle activations throughout their first year of life when stepping or kicking, even when producing only alternated leg movements (Sylos-Labini et al., 2020; Teulier et al., 2012). This multilevel variability can arise in numerous environmental contexts and is associated with the development of multiple components, like the growth of musculoskeletal structures, the myelination of neural circuits, or the motivational goal to move, leading infants to learn new skills with their own developmental time scale (Adolph et al., 2018).

The third type of variability, corresponding to the ability of the human body to produce a given movement in various ways, is permitted by the existence of numerous nerves and muscles that can control a given joint, which is often referred to as motor control redundancy (Bernstein, 1967). In adulthood, the central nervous system (CNS) seems to simplify the coordination of these numerous degrees of freedom (DOFs) via a small number of encoded primitives, also called motor modules or muscle synergies (Bizzi et al., 1991; d’Avella et al., 2003; Tresch et al., 1999). A primitive is a neural structure that is stored within the CNS at a spinal level and that autonomously produces a coordinated pattern of behavior (i.e. involving several muscles) when recruited from higher centers (Bizzi et al., 2008). In adult organisms, primitives seem to be encoded within the spinal cord and the brainstem (Bizzi et al., 1991; Hart and Giszter, 2010; Mussa-Ivaldi et al., 1994; Roh et al., 2011) and activated by the motor cortex (Drew et al., 2008; Overduin et al., 2015; Overduin et al., 2012) as well as regulated by sensory feedback (Cheung et al., 2005). In humans, the physical location of such primitives remains unknown, but computational modeling from electromyographic (EMG) data also suggests the existence of a modular command (Berger et al., 2013; Ivanenko et al., 2004). Two types of modules are described: a spatial module is a group of muscles that are activated together with relative weights, while a temporal module is a waveform that describes the activation of a spatial module across time (Delis et al., 2014). In adults walking, the EMG activity of numerous muscles of the lower limb can be efficiently reproduced by 4–5 spatial and temporal modules (Dominici et al., 2011; Hinnekens et al., 2020; Lacquaniti et al., 2012; Neptune et al., 2009; Figure 1).

Figure 1

Download asset Open asset

The development of this fine-tuned modular organization has been investigated from birth on, and several neonatal behaviors have been found to already rely on a low-dimensional modular organization. In particular, stepping and kicking are two neonatal behaviors that can involve alternate flexion and extension cycles of the lower limb, stepping being elicited by a pediatrician when the infant is held in an erected position while kicking is a natural behavior performed in supine position. Those behaviors were found to each involve modules that present similarities with mature modules, making them both distinct locomotor precursors (Dominici et al., 2011; Sylos-Labini et al., 2020). For example, neonatal stepping is based on two modules while walking in toddlers is based on four, suggesting that the motor repertoire of newborns is restricted (Dominici et al., 2011). This is coherent with the fact that innate behaviors are described as stereotyped (Jeng et al., 2002; Spencer and Thelen, 2000; Thelen et al., 1981), with strong coupling among joints (Fetters et al., 2004; Jeng et al., 2002; Thelen et al., 1981) and among agonist/antagonist muscles (Teulier et al., 2012), while typically developing infants will develop the ability to dissociate their degrees of freedom toward a richer repertoire (Fetters et al., 2004). However, the existence of a low-dimensional modular organization in newborns was established on single-step or averaged data (Dominici et al., 2011; Sylos-Labini et al., 2020), while the muscular activity that underlies neonatal movements is known to be highly variable, even for a given coordination mode like, for example, across alternated leg movements (Teulier et al., 2012). This intra-individual variability is believed to be a key feature of typical motor development allowing learning (Dhawale et al., 2017; Hadders-Algra, 2018). According to animal studies, variability could even be centrally regulated for the purpose of motor exploration (Kao et al., 2008; Mandelblat-Cerf et al., 2009).

If modularity and variability seem antagonistic at first glance, the question of whether those two features are compatible or incompatible needs to be addressed to better understand motor development. On one hand, a low-dimensional modular system inherently limits motor exploration (Cohn et al., 2018; Valero-Cuevas, 2009). In this vein, the high variability of EMG data during the first year of life opened discussion about the existence itself of motor primitives (Teulier et al., 2012). On the other hand, data from animals suggest that such variability could be generated within a modular system during development. A variable output was indeed observed after applying different stimuli to the neonatal spinal cord of rodents (Kiehn and Kjaerulff, 1996; Klein et al., 2010), which is also believed to store motor primitives (Blumberg et al., 2013; Dominici et al., 2011). In young songbirds, a specialized cortical area has even been found to be responsible for inserting variability into the temporal structure of vocalization to facilitate learning in early development, resulting in a highly variable output that becomes structured when inhibiting the area (Aronov et al., 2011; Kao et al., 2008). As those data suggest that is possible to produce variability by modulating the activation of basic inputs, such organization might shape the development of the motor system in human infants.

In human infants, investigations of the motor system are more limited and EMG recordings are the closest signals to the neural output that can be recorded while moving. Yet, if modularity and variability do coexist within the neural command, we should be able to separate the contribution of motor primitives from the variability of EMG signals and observe their cross-evolution during development. To test this prediction, we longitudinally followed 18 human infants and recorded the EMG activity of 10 lower-limb muscles on 2–3 time points between birth and walking onset, during stepping, kicking, or walking (Figure 2). Using a state-of-the-art unsupervised machine learning approach, we were able to model the underlying command by decomposing the EMG data of numerous muscles into step-invariant basic muscle patterns (which represent the motor primitives at each age) and into step-variable activation coefficients (which theoretically represent the variable descending command that modulates the activation of the motor primitives, at least in adults) (d’Avella et al., 2003; Delis et al., 2014; Figure 1). We describe the evolution of both motor variability and motor modularity from birth to independent walking and provide evidence that the human motor system could theoretically initiate its exploration by variably activating a few temporary basic structured patterns.

Figure 2

Download asset Open asset

Figure 2—source data 1

Individual data regarding basic electromyographic (EMG) and kinematic parameters (corresponding to Figure 2F–H).

https://cdn.elifesciences.org/articles/87463/elife-87463-fig2-data1-v1.xlsx

Download elife-87463-fig2-data1-v1.xlsx

Eighteen infants were tested longitudinally on 2–3 time points between birth (~4 d) and walking onset (~14 mo). The time points were either around birth, around 3 mo, or around walking onset (individual characteristics and precise time points are reported in Table 2). Around birth and 3 months old, we observed the stepping behavior and/or the kicking behavior, while at walking onset we only recorded independent walking. In each behavior and at each age, infant movements were recorded using surface EMG on 10 bilateral lower-limb muscles and two 2D video cameras. Based on the resulting films, trained coders selected alternated cycles of flexion and extension of the lower limbs, which allowed us to study the same movement regardless of the behavior that could be produced by the infant at each age and focus only on the generation of trial-to-trial variability for this given movement. Data from a given baby were considered analyzable when we had recorded clean surface EMG signals of the 10 lower-limb muscles during at least five alternated cycles of flexion and extension, both at birth and 3 months old and through the same behavior (stepping or kicking). Those cycles were not necessarily consecutive, but to be selected a given cycle had to be at least preceded by an extension and succeeded by a flexion. In total, 586 cycles of flexion and extension were included into the analysis. When more than five cycles were available, we proceeded by analyzing random combinations of five cycles among the available ones and averaging the results afterward, so that the variability would always be calculated on a same number of cycles. For each behavior, we computed the variability of the motor output (index of EMG variability [IEV]) and used non-negative matrix factorization (NNMF) to identify the underlying motor primitives and their activation parameters. We computed a goodness-of-fit criterion to establish whether the cycle-to-cycle variability of five cycles of flexion and extension of the lower limbs could be produced through various combinations of those motor primitives. We compared this goodness-of-fit criterion across ages and computed other indexes in order to characterize (1) how variably were those motor primitives activated and (2) how selective were those primitives (i.e. if they controlled numerous muscles at a time or a few muscles). Table 1 summarizes the role of each of the main variables. Details are available in the ‘Materials and methods’ section.

Kinematic parameters and EMG signals reveal maximal motor variability during the neonatal period

We started by characterizing basic kinematic and EMG parameters at each age and in each behavior. Wilcoxon tests were performed among kinematic parameters (cycle duration and its variability, proportion of extension/flexion phases) to assess basic differences. The cycle duration was different across behaviors with a decrease from stepping at birth to stepping at 3 mo (p=0.01) and to walking in toddlers (p<0.001) as well as a decrease from kicking at birth to kicking at 3 mo (p=0.003) and to walking in toddlers (p=0.009, Figure 2F). The proportion of phases within a cycle was slightly different across ages (Figure 2H, Supplementary file 1a).

The kinematic variability was assessed by the variability of cycle duration (Figure 2G). This variability significantly decreased for stepping and kicking from 3 mo to walking onset in toddlers (p<0.001). Between birth and 3 months old, this variability seems to have begun to decrease (p=0.021 for kicking, and trend of p=0.083 for stepping). The variability of EMG data was assessed by the IEV ( Figure 3D). This index significantly decreased from 3 mo to walking onset in both stepping and kicking (p=0.003 and p=0.001 respectively). However, it significantly decreased between birth and 3 months old for stepping (p=0.005) and not for kicking in which the evolution seems to be different across individuals (p=0.519, Figure 3D).

Figure 3

Download asset Open asset

Figure 3—source data 1

Individual data regarding electromyographic (EMG) output and modeling of the modular organization from birth to walking onset (corresponding to Figure 3B–D).

https://cdn.elifesciences.org/articles/87463/elife-87463-fig3-data1-v1.xlsx

Download elife-87463-fig3-data1-v1.xlsx

Figure 4 with 2 supplements see all

Download asset Open asset

Motor primitives evolve between birth and walking onset toward gathering less muscles at a time

In order to identify whether motor primitives would have been preserved across ages, we applied the best matching pairs method (Cheung et al., 2005) to our data and checked for similitudes between modules. As we could not find high similitudes, we noticed that modules seemed to be less and less complex with time, suggesting a more individual muscle control (i.e. spatial modules gathered the activation of less and less muscles, and temporal modules represented tighter and tighter peaks of activation, Figure 5A). To quantify this phenomenon, we created two indexes: the selectivity of muscular activations indexes (SMAI) and the selectivity of temporal activation indexes (STAI) (see Figure 5—source data 1 for individual data). The SMAI increased after 3 mo, indicating that muscle weightings were sparser among spatial modules in toddlers than in infants. In other words, spatial modules were mostly composed of fewer muscles in toddlers compared to infants (see Figure 5A and B). This increase occurred from stepping and kicking at birth to walking (p=0.002 and p<0.001, respectively) and from stepping or kicking at 3 mo to walking (p<0.001). No significant effect was found between birth and 3 mo for stepping or kicking even if a trend appeared for stepping (p=0.054). Analyses were repeated on modular decompositions coming from the systematic extraction of four spatial and temporal modules and gave the same results (Supplementary file 1a).

Figure 5

Download asset Open asset

Figure 5—source data 1

Individual data regarding modules’ structure from birth to walking onset (corresponding to Figure 5B and C).

https://cdn.elifesciences.org/articles/87463/elife-87463-fig5-data1-v1.xlsx

Download elife-87463-fig5-data1-v1.xlsx

The STAI also increased from birth and 3 mo to walking. This indicates that temporal modules had more tightened peaks of activation in toddlers than in infants (Figure 5A and C). The value for toddlers was also above the adult landmark (Figure 5C). For both stepping and kicking, the increase occurred between birth and walking (p=0.004 and p<0.001, respectively) and between 3 mo and walking (p<0.001). Analyses were repeated following the extraction of four spatial and temporal modules and gave the same results (Supplementary file 1a).

The objective of this study was to investigate whether and when trial-to-trial variability could emerge from the modular system of developing human infants. We found that the EMG variability of lower-limb muscles was maximal in the neonatal period and decreased at walking onset. This decrease in trial-to-trial variability was associated with an increase in the number of motor primitives. In the neonatal period the maximal EMG variability could be explained by variable activations and combinations of a small number of motor primitives, suggesting that the motor system can generate trial-to-trial variability at birth albeit relying on few primitives. In contrast at walking onset, more primitives were needed to explain EMG patterns of alternated leg movements, and these primitives were activated more consistently across trials. Furthermore, primitives were more selective than in infanthood, suggesting that toddlers developed the capacity to control less muscles at a time. Together, these results provide evidence that the motor system is flexible as soon as birth despite the existence of basic patterns of coordination and suggest that neonatal motor primitives are plastic structures that fraction in early life to become more selective and more steadily activated. Below we discuss these findings in relation with the current literature about motor exploration and the maturation of the motor system through development.

This study highlights that EMG signals of alternated leg movements in human infants are highly variable as soon as birth, while neonatal behavior is often described as rather stereotyped due to the high prevalence of cocontractions (Dominici et al., 2011; Jeng et al., 2002; Spencer and Thelen, 2000; Thelen et al., 1981). Moreover, the signals analyzed here showed high variability while they were screened and selected among a lot of cycles that could be parallel or unilateral rather than alternate, which demonstrates even more flexibility than what we analyzed (Siekerman et al., 2015; Thelen and Fisher, 1983a; Thelen et al., 1981). If we assume that trial-to-trial variability is a marker of motor exploration (Dhawale et al., 2017; Kao et al., 2008), our results would suggest that exploring the field of possible movements is possible as soon as birth despite the difficulty in uncoupling the different muscles and joints of lower limbs (Fetters et al., 2004; Teulier et al., 2012; Vaal et al., 2000). Such idea is coherent with the fact that motor learning abilities are in place very early in development and even before birth (Robinson, 2015; Robinson and Kleven, 2005).

Our results revealed that the most variable EMG output was seen at the time where the fewest number of primitives was found (i.e. in the neonatal period). This confirms and extends previous analyses relying on cross-sectional data showing that the motor system involves a few modules in rat and human newborns when producing alternated leg movements (Dominici et al., 2011; Sylos-Labini et al., 2020) and is in coherent with recent results showing that apparently noisy data could stem a low-dimensional organization in human infants (Sylos-Labini et al., 2022). It also shows that trial-to-trial variability in humans can be generated through a structured modular system similarly to newborn rats (Blumberg et al., 2013), which seems to be specific to infanthood (as opposed to toddlerhood) according to our results. Although the neural interpretation is limited here, motor primitives were originally identified as spinal structures in animals (Bizzi et al., 1991) and are still hypothesized to be stored at a spinal level and activated through supraspinal inputs in adults (Bizzi and Cheung, 2013; Delis et al., 2014; Overduin et al., 2015; Overduin et al., 2012; Roh et al., 2011). As such our results are reminiscent with neurophysiological studies in rodents that demonstrate that different motor patterns can emerge by applying different pharmacological or electrical stimuli to the neonatal spinal cord (Kiehn and Kjaerulff, 1996; Klein et al., 2010) and that the neonatal motor system is capable of flexibility via the variable activations of a few basic structures (Blumberg et al., 2013). Such variable activations could be due to the instability of the command and be incidental, but it could also be genuinely purposeful: in young birds learning to sing, the output appears to be highly variable, but becomes even more structured than the adult one after having inhibited some specific areas of the brain, proving that such areas were responsible for generating variability and facilitating exploration (Aronov et al., 2011). With growth, adaptive inhibitory mechanisms settle in a way that does not compromise the capacity for future plasticity (Garst-Orozco et al., 2014). As similar neurophysiological investigations cannot be conducted in human infants, discriminating among purposeful and incidental variability remains challenging. However, the present results demonstrate that the motor output of neonates can also be decomposed into highly structured patterns and their temporary variable activations, which shed new light about the potential existence of similar mechanisms. As we observed such structure within alternated leg movements, other studies are needed to explore the extent of these results to other early behaviors or coordination modes, as well as their link with the ability to move in various environmental contexts.

Strikingly, we observe the reverse phenomenon in toddlerhood: as infants grew to become toddlers, they seem to have widened their motor repertoire along with a stabilization of their command to produce alternate leg movements. Indeed, more modules were needed to explain the EMG signals than in infanthood (Figure 3B), similarly to what was found in Dominici et al., 2011. The same trend was observed when considering stepping or kicking, which were both shown to be neonatal locomotor precursors (Sylos-Labini et al., 2020). The increase in the number of modules along the first year of life was associated with the changes within the shape of modules themselves. They became more selective at walking onset (Figure 5), suggesting a more individual muscle control. This is in accordance with previous results reporting fractionation of primitives and decrease of duration of temporal modules (Sylos-Labini et al., 2020) as well as consistent with the known decrease in cocontractions across the first year of life (Teulier et al., 2012). This is also strongly reminiscent with recent findings regarding the development of running, which starts with a fractionation of motor primitives between childhood and adulthood, allowing those primitives to be merged again later with training (Cheung et al., 2020a). On a different scale of analysis that was recently allowed by the use of high-density EMG in human neonates, it was also established that the synchronization across motoneurons of a given muscle was more important in neonates than in adults, which suggests a reshaping of central pattern generators during development (Del Vecchio et al., 2020). Assuming that modules are stored at a spinal level, such a reshaping within modules themselves is likely to be made early in infancy. Spinal circuits are indeed particularly plastic during early development thanks to the high activity of neurophysiological processes that can lead to changes within neurons’ excitability through practice (Brumley et al., 2015; Vinay et al., 2002). As neonates are able to set up several behaviors that involve flexion and extension cycles of lower limbs, such as stepping and kicking but also air-stepping, crawling, or swimming (Barbu-Roth et al., 2014; Forma et al., 2019; Forma et al., 2018; McGraw, 1941; McGraw, 1939), they could already benefit from a lot of opportunities to explore and practice in order to shape the underlying circuits. The particular plasticity of motor primitives at this point is most likely allowed by the absence of stable synaptic connections within pathways, synaptogenesis being particularly active in the neonatal period (An et al., 2012; de Graaf-Peters and Hadders-Algra, 2006).

The cross-development between trial-to-trial variability of activations and the number of primitives suggests that the motor system is never designed to produce a maximal amount of variability. One could indeed picture a scenario where a maximal number of primitives would have been associated with maximally variable activations of these modules to maximize variability and exploration (or even no primitives at all to allow free muscle activations). However, constraining the space of possible options might be crucial for motor learning (Bernstein, 1967; Dhawale et al., 2017), which also relates to the exploration–exploitation tradeoff in reinforcement learning (Sutton and Barto, 1998). Humanoid robots indeed learn and build motor synergies more easily when starting to explore with a limited number of available degrees of freedom such as dynamic movement primitives (Lapeyre et al., 2011; Lungarella and Berthouze, 2002; Schaal, 2006). The developing system might have to always remain in an ideal compromise of constraint and flexibility, the difficulty of uncoupling lower-limb joints at the beginning of life (Fetters et al., 2004; Vaal et al., 2000) being an example of such helping constraint in humans. It was suggested that this rigid coupling that reduces the effective number of DOF allows to move with less need of processing capacities and without interferences of uncoordinated outputs (Piek, 2002). If this constraint might have a maturational origin, many factors could provide the same type of DOF reduction early in development (Newell et al., 1989), such as environmental ones (e.g. the space within the womb environment or the gravity at birth) and factors related to the task (e.g. kicking might allow to focus on lower limbs only while walking also requires balance). Here we propose that the benefit of developmental motor primitives might be to yield an ideal space of possibilities that allow to efficiently explore several motor solutions via trial-to-trial variations of the activations of those temporary primitives. This might be critical to shape motor circuits, as suggested by the correlation between the lack of motor variability following early cerebral lesions and poor developmental outcomes in humans (Einspieler and Prechtl, 2005). In this context, the approach proposed here could contribute to the quantitative study of motor variability in atypical development (Hadders-Algra, 2008) and characterize the extent to which motor exploration can be elicited in developing early rehabilitation protocols that are based on rhythmic behaviors (Angulo-Barroso et al., 2013; Campbell et al., 2012; Kolobe and Fagg, 2019; Sargent et al., 2020; Teulier et al., 2009).

When working with EMG to identify hypothetical motor primitives, several factors can limit interpretations such as EMG processing, cross-talk, or arbitrary choices made during EMG factorization. It is indeed worth noticing that the current study reports different values than previous developmental studies regarding the absolute number of modules (Dominici et al., 2011; Sylos-Labini et al., 2020). However, there is currently no consensus regarding the selection of the number of modules that depends on arbitrary criteria such as the VAF threshold. We can also note that Sylos-Labini et al., 2020 reported more modules in kicking than in stepping at birth in contrast to what is reported here; however, strict comparisons are limited on this point since we only studied supine kicking, whereas these authors considered kicking both supine and vertically held. Also, to deal with the lack of consensus about EMG processing, we reproduced our analysis with other choices regarding filtering, amplitude normalization, and time normalization, and verified that similar effects and trends emerged. This verification confirmed that our results were robust to reasonable processing choices. Cross-talk, which refers to the possibility of recording several muscles with one electrode, is particularly challenging in developmental studies since growth implies a modification of the distance across muscles. To deal with this issue, we purposely chose muscles from different body regions (the 10 muscles are distributed from the hips to the shank and over the two lower limbs). For agonist and antagonist muscles of the same body regions, we reproduced the analysis that was proposed by Dominici et al., 2011 ensuring that raw signals were not correlated (see ‘Materials and methods’). Despite all precautions, the size of surface EMG electrodes remains a challenging issue of the field, and current development of high-density EMG might offer interesting perspectives for collecting more and more precise signals through surface EMG (Del Vecchio et al., 2020).

Overall, when compared with adult values (Figure 3, Supplementary file 1c), our results suggest an immaturity of the modular system before and around walking onset, which confirms that infancy should be an ideal period of plasticity to benefit from in therapy (Ulrich, 2010; Morgan et al., 2021). These are also coherent with the idea that skill onset during development in not an on-off switch and that maturity is not reached as soon as a skill becomes possible (Adolph et al., 2018). Interestingly, the longitudinal design of this study highlighted some inter-individual differences among the evolution of kicking during the first months of life, whereas the overall variability of the motor output decreased in stepping. For example, the IEV decreased between kicking at birth and kicking at 3 months old for a part of the cohort while it increased for the other part (Figure 3D). These inter-individual differences were observed for the main variables of the study, including the number of primitives and the variability of activation (Supplementary file 1a). As kicking is a spontaneous behavior, these differences might relate with the fact that it is differently practiced across individuals. Some infants might indeed store more modules than others due to different amount of practice, while stepping would evolve toward more stable activations of more modules in every infant, coherently with the fact that the two behaviors are distinct locomotor precursors (Sylos-Labini et al., 2020). However, the longitudinal follow-up also showed that the structure of variability evolved in the same direction for every individual between 3 months old and walking onset, with the development of more modules that were more steadily activated, regardless of the locomotor precursor that was recorded at 3 months old. Interestingly, this indicates that the modular system seems to be in a common state for every individual around walking onset, despite the important variability of age in our cohort (Table 2). This state of high dimensionality might last for a non-negligible period of time. Recently, studying the development of walking and running in children, the authors identified more primitives at 2 and 5 years old than in adults (Bach et al., 2021). As Cheung et al., 2020b showed that running motor primitives merge with training, we can hypothesize that walking primitives of toddlers will also merge with practice over time. Since learning to walk continues long after walking onset (Chang et al., 2006; Müller et al., 2013), it is not surprising to notice that the modular organization of toddlers could still need adjustments, even though it suggests that the system might first need to develop the capacity to control muscles more separately before gathering them again into more complex modules. As learning in a modular system relies on both learning the shape of modules and learning their activation parameters (d’d’Avella and Pai, 2010), the two processes might not be concomitant to ensure, as suggested above, to always remain in an ideal space of possibilities. Interestingly, recent data from rats report similar modular organization between organisms with different developmental history, suggesting that spinal primitives are determined early in development and conserved into adulthood (Yang et al., 2019). However, the fact that the neural repertoire is not mature before the important neural pruning of late adolescence in humans (de Graaf-Peters and Hadders-Algra, 2006) suggests that module shaping could continue over a long period of time. The present study falls into this converging framework of long-lasting plasticity of motor modules, associating fractionation and merging of motor primitives throughout development and training (Bach et al., 2021; Cheung et al., 2020b; Dominici et al., 2011; Hinnekens et al., 2020; Sylos-Labini et al., 2020). While more studies are needed to characterize the maturation of the modular system after walking onset in humans, a long-lasting plasticity of motor primitives could allow to adapt to the development of the musculoskeletal system (Bizzi et al., 1991; Bizzi and Cheung, 2013) in order to integrate biomechanical specificities of each individual (Torres-Oviedo and Ting, 2010) as well as optimality considerations allowing the low-energy costs of mature walking (Berret et al., 2019; Catavitello et al., 2018; de Rugy et al., 2012; Selinger et al., 2015).

The protocol was in accordance with the Declaration of Helsinki and approved by the French Committee of People Protection. Families were recruited at the Port-Royal maternity in Paris. For each child, a parent provided informed written consent to participate to the study.

Experimental design

Request a detailed protocol

For each experiment, the first stage was to equip the infant or toddler with EMG sensors. Infants were then observed in several positions. At birth and 3 mo, they were observed in a supine position to observe kicking as well as held upright to induce stepping. We also observed two other behaviors that are not reported in this study (crawling and stepping on a treadmill). Each behavior was observed on a pediatric table during about 2 min in a randomized order for each infant and at each age. Shortly after walking onset, toddlers were asked to go back and forth along a 2 m exercise mat. They were walking barefoot at a natural speed during approximately 1 min without any help from adults. Examples of each behavior are shown in Video 1 (kicking at birth and around 3 months old in participant 5) and Video 2 (stepping at birth and around 3 months old in participant 6 followed by walking at walking onset).

Video 1

Download asset

Video 2

Download asset

EMG processing

Request a detailed protocol

EMG data are presented in Figure 2, and the procedure for EMG processing is presented in Figure 6. For each signal, we applied a high-pass filter (40 Hz, fourth-order Butterworth filter) followed by a rectification, as in Ivanenko et al., 2013. To smooth envelopes, we used a moving median since some artifacts were often visible in kicking due to the leg touching the other leg or the mat. The window of this moving median was normalized relatively to the cycle duration that changed with the age of the infant, as described in Ivanenko et al., 2013. As cycle duration was about twice lower on average in 3-month-old infants, and again twice lower in toddlers, the moving median window was 400 points in newborns, 200 points in 3 month olds, and 100 points in toddlers. This filtering procedure was applied to every available cycle of each infant and for each age. Signals were normalized for each ensemble of five cycles that were included in the analyses. Finally, cycles were isolated and normalized in time so that each cycle would correspond to 200 time points by interpolating the flexion phase to 80 points and the extension phase to 120 points based on the proportion of phases of independent walking. The rationale here is that all cycles will temporally match the same kinematic events regardless of the age or the behavior. To test how choices on amplitude and time normalization could affect our results, we repeated the analyses of this article with a different amplitude normalization (normalizing under the whole available signals instead of ensembles of five cycles) and with a different time normalization (interpolating to the cycle without fixing the phases), which confirmed that our results were not dependent of those methodological choices.

Figure 6

Download asset Open asset

As cross-talk might be an issue when recording surface EMG data, we used the same criterion of Dominici et al., 2011 to assess potential cross-talk (Pearson correlation coefficient >0.2 among pairs of agonist and antagonist muscles). At birth, 3% of our sample had a correlation coefficient >0.2; 1.4% at 3 months, and 6% at walking onset. For these samples, we checked whole recordings and verified that different strides from one subject were not all >0.2.

Individual data supporting the findings of this study are included in the manuscript, supporting file, and source data files of Figures 2, 3, and 5. The full dataset underlying the computational analysis (EMG data) has been uploaded to https://zenodo.org/record/8193532. The custom code underlying the computational analysis is available as Source code 1.

1. 1. Hinnekense E
   2. Barbu-Roth M
   3. M-C Do
   4. Berret B
   5. Teulier C

   (2023) Zenodo

   Dataset underlying the paper Generating variability from motor primitives during infant locomotor development.

   https://doi.org/10.5281/zenodo.8193532

1. 1. Adolph KE
   2. Hoch JE
   3. Cole WG

   (2018) Development (of walking): 15 suggestions

   Trends in Cognitive Sciences 22:699–711.

   https://doi.org/10.1016/j.tics.2018.05.010

   • PubMed
   • Google Scholar
2. 1. An S
   2. Yang J-W
   3. Sun H
   4. Kilb W
   5. Luhmann HJ

   (2012) Long-term potentiation in the neonatal rat barrel cortex in vivo

   The Journal of Neuroscience 32:9511–9516.

   https://doi.org/10.1523/JNEUROSCI.1212-12.2012

   • PubMed
   • Google Scholar
3. 1. Angulo-Barroso RM
   2. Tiernan C
   3. Chen LC
   4. Valentin-Gudiol M
   5. Ulrich DA

   (2013) Treadmill training in moderate risk preterm infants promotes stepping quality--results of a small randomised controlled trial

   Research in Developmental Disabilities 34:3629–3638.

   https://doi.org/10.1016/j.ridd.2013.07.037

   • PubMed
   • Google Scholar
4. 1. Aronov D
   2. Veit L
   3. Goldberg JH
   4. Fee MS

   (2011) Two distinct modes of forebrain circuit dynamics underlie temporal patterning in the vocalizations of young songbirds

   The Journal of Neuroscience 31:16353–16368.

   https://doi.org/10.1523/JNEUROSCI.3009-11.2011

   • PubMed
   • Google Scholar
5. 1. Bach MM
   2. Daffertshofer A
   3. Dominici N

   (2021) Muscle synergies in children walking and running on a treadmill

   Frontiers in Human Neuroscience 15:637157.

   https://doi.org/10.3389/fnhum.2021.637157

   • PubMed
   • Google Scholar
6. 1. Barbu-Roth M
   2. Anderson DI
   3. Desprès A
   4. Streeter RJ
   5. Cabrol D
   6. Trujillo M
   7. Campos JJ
   8. Provasi J

   (2014) Air stepping in response to optic flows that move Toward and Away from the neonate

   Developmental Psychobiology 56:1142–1149.

   https://doi.org/10.1002/dev.21174

   • PubMed
   • Google Scholar
7. 1. Berger DJ
   2. Gentner R
   3. Edmunds T
   4. Pai DK
   5. d’Avella A

   (2013) Differences in adaptation rates after virtual surgeries provide direct evidence for modularity

   The Journal of Neuroscience 33:12384–12394.

   https://doi.org/10.1523/JNEUROSCI.0122-13.2013

   • PubMed
   • Google Scholar
8. Book

   1. Bernstein NA

   (1967)

   The Co-Ordination and Regulation of Movements

   Pergamon Press.

   • Google Scholar
9. Book

   1. Berret B
   2. Delis I
   3. Gaveau J
   4. Jean F

   (2019) Optimality and Modularity in human movement: from optimal control to muscle synergies in

   In: Venture G, Laumond JP, Watier B, editors. Biomechanics of Anthropomorphic Systems. Cham: Springer. pp. 105–133.

   https://doi.org/10.1007/978-3-319-93870-7

   • Google Scholar
10. 1. Bizzi E
    2. Mussa-Ivaldi FA
    3. Giszter S

    (1991) Computations underlying the execution of movement: a biological perspective

    Science 253:287–291.

    https://doi.org/10.1126/science.1857964

    • PubMed
    • Google Scholar
11. 1. Bizzi E
    2. Cheung VCK
    3. d’Avella A
    4. Saltiel P
    5. Tresch M

    (2008) Combining modules for movement

    Brain Research Reviews 57:125–133.

    https://doi.org/10.1016/j.brainresrev.2007.08.004

    • PubMed
    • Google Scholar
12. 1. Bizzi E
    2. Cheung VCK

    (2013) The neural origin of muscle synergies

    Frontiers in Computational Neuroscience 7:51.

    https://doi.org/10.3389/fncom.2013.00051

    • PubMed
    • Google Scholar
13. 1. Blumberg MS
    2. Coleman CM
    3. Gerth AI
    4. McMurray B

    (2013) Spatiotemporal structure of REM sleep twitching reveals developmental origins of motor synergies

    Current Biology 23:2100–2109.

    https://doi.org/10.1016/j.cub.2013.08.055

    • PubMed
    • Google Scholar
14. 1. Brumley MR
    2. Kauer SD
    3. Swann HE

    (2015) Developmental plasticity of coordinated action patterns in the perinatal rat

    Developmental Psychobiology 57:409–420.

    https://doi.org/10.1002/dev.21280

    • PubMed
    • Google Scholar
15. 1. Campbell SK
    2. Gaebler-Spira D
    3. Zawacki L
    4. Clark A
    5. Boynewicz K
    6. deRegnier R-A
    7. Kuroda MM
    8. Bhat R
    9. Yu J
    10. Campise-Luther R
    11. Kale D
    12. Bulanda M
    13. Zhou XJ

    (2012) Effects on motor development of kicking and stepping exercise in preterm infants with periventricular brain injury: A pilot study

    Journal of Pediatric Rehabilitation Medicine 5:15–27.

    https://doi.org/10.3233/PRM-2011-0185

    • PubMed
    • Google Scholar
16. 1. Catavitello G
    2. Ivanenko Y
    3. Lacquaniti F

    (2018) A kinematic synergy for terrestrial locomotion shared by mammals and birds

    eLife 7:e38190.

    https://doi.org/10.7554/eLife.38190

    • PubMed
    • Google Scholar
17. 1. Chang CL
    2. Kubo M
    3. Buzzi U
    4. Ulrich BD

    (2006) Early changes in muscle activation patterns of toddlers during walking

    Infant Behavior & Development 29:175–188.

    https://doi.org/10.1016/j.infbeh.2005.10.001

    • PubMed
    • Google Scholar
18. 1. Cheung VCK
    2. d’Avella A
    3. Tresch MC
    4. Bizzi E

    (2005) Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors

    The Journal of Neuroscience 25:6419–6434.

    https://doi.org/10.1523/JNEUROSCI.4904-04.2005

    • PubMed
    • Google Scholar
19. 1. Cheung VCK
    2. Cheung BMF
    3. Zhang JH
    4. Chan ZYS
    5. Ha SCW
    6. Chen CY
    7. Cheung RTH.

    (2020a) Plasticity of muscle synergies through fractionation and merging during development and training of human runners

    Nat Commun 11:1–15.

    https://doi.org/10.1038/s41467-020-18210-4

    • Google Scholar
20. 1. Cheung VCK
    2. Zheng XC
    3. Cheung RTH
    4. Chan RHM

    (2020b) Modulating the structure of motor variability for skill learning through specific muscle synergies in elderlies and young adults

    IEEE Open Journal Of Engineering in Medicine and Biology 1:33–40.

    https://doi.org/10.1109/OJEMB.2019.2963666

    • Google Scholar
21. 1. Clark DJ
    2. Ting LH
    3. Zajac FE
    4. Neptune RR
    5. Kautz SA

    (2010) Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke

    Journal of Neurophysiology 103:844–857.

    https://doi.org/10.1152/jn.00825.2009

    • PubMed
    • Google Scholar
22. 1. Cohn BA
    2. Szedlák M
    3. Gärtner B
    4. Valero-Cuevas FJ

    (2018) Feasibility theory reconciles and informs alternative approaches to neuromuscular control

    Frontiers in Computational Neuroscience 12:62.

    https://doi.org/10.3389/fncom.2018.00062

    • PubMed
    • Google Scholar
23. 1. d’Avella A
    2. Saltiel P
    3. Bizzi E

    (2003) Combinations of muscle synergies in the construction of a natural motor behavior

    Nature Neuroscience 6:300–308.

    https://doi.org/10.1038/nn1010

    • Google Scholar
24. 1. d’Avella A
    2. Pai DK

    (2010) Modularity for sensorimotor control: evidence and a new prediction

    Journal of Motor Behavior 42:361–369.

    https://doi.org/10.1080/00222895.2010.526453

    • Google Scholar
25. 1. de Graaf-Peters VB
    2. Hadders-Algra M

    (2006) Ontogeny of the human central nervous system: What is happening when?

    Early Human Development 82:257–266.

    https://doi.org/10.1016/j.earlhumdev.2005.10.013

    • Google Scholar
26. 1. Del Vecchio A
    2. Sylos-Labini F
    3. Mondì V
    4. Paolillo P
    5. Ivanenko Y
    6. Lacquaniti F
    7. Farina D

    (2020) Spinal motoneurons of the human newborn are highly synchronized during leg movements

    Science Advances 6:eabc3916.

    https://doi.org/10.1126/sciadv.abc3916

    • PubMed
    • Google Scholar
27. 1. de Rugy A
    2. Loeb GE
    3. Carroll TJ

    (2012) Muscle coordination is habitual rather than optimal

    The Journal of Neuroscience 32:7384–7391.

    https://doi.org/10.1523/JNEUROSCI.5792-11.2012

    • PubMed
    • Google Scholar
28. 1. Delis I
    2. Panzeri S
    3. Pozzo T
    4. Berret B

    (2014) A unifying model of concurrent spatial and temporal modularity in muscle activity

    Journal of Neurophysiology 111:675–693.

    https://doi.org/10.1152/jn.00245.2013

    • PubMed
    • Google Scholar
29. 1. Dhawale AK
    2. Smith MA
    3. Ölveczky BP

    (2017) The role of variability in motor learning

    Annual Review of Neuroscience 40:479–498.

    https://doi.org/10.1146/annurev-neuro-072116-031548

    • PubMed
    • Google Scholar
30. 1. Dominici N
    2. Ivanenko YP
    3. Cappellini G
    4. d’Avella A
    5. Mondì V
    6. Cicchese M
    7. Fabiano A
    8. Silei T
    9. Di Paolo A
    10. Giannini C
    11. Poppele RE
    12. Lacquaniti F

    (2011) Locomotor primitives in newborn babies and their development

    Science 334:997–999.

    https://doi.org/10.1126/science.1210617

    • PubMed
    • Google Scholar
31. 1. Drew T
    2. Kalaska J
    3. Krouchev N

    (2008) Muscle synergies during locomotion in the cat: A model for motor cortex control

    The Journal of Physiology 586:1239–1245.

    https://doi.org/10.1113/jphysiol.2007.146605

    • Google Scholar
32. 1. Einspieler C
    2. Prechtl HFR

    (2005) Prechtl’s assessment of general movements: A diagnostic tool for the functional assessment of the young nervous system

    Mental Retardation and Developmental Disabilities Research Reviews 11:61–67.

    https://doi.org/10.1002/mrdd.20051

    • PubMed
    • Google Scholar
33. 1. Fetters L
    2. Chen Y
    3. Jonsdottir J
    4. Tronick EZ

    (2004) Kicking coordination captures differences between full-term and premature infants with white matter disorder

    Human Movement Science 22:729–748.

    https://doi.org/10.1016/j.humov.2004.02.001

    • PubMed
    • Google Scholar
34. 1. Forma V
    2. Anderson DI
    3. Goffinet F
    4. Barbu-Roth M

    (2018) Effect of optic flows on newborn crawling

    Developmental Psychobiology 60:497–510.

    https://doi.org/10.1002/dev.21634

    • PubMed
    • Google Scholar
35. 1. Forma V
    2. Anderson DI
    3. Provasi J
    4. Soyez E
    5. Martial M
    6. Huet V
    7. Granjon L
    8. Goffinet F
    9. Barbu-Roth M

    (2019) What does prone skateboarding in the newborn tell us about the ontogeny of human locomotion?

    Child Development 90:1286–1302.

    https://doi.org/10.1111/cdev.13251

    • PubMed
    • Google Scholar
36. 1. Garst-Orozco J
    2. Babadi B
    3. Ölveczky BP

    (2014) A neural circuit mechanism for regulating vocal variability during song learning in zebra finches

    eLife 3:e03697.

    https://doi.org/10.7554/eLife.03697

    • PubMed
    • Google Scholar
37. 1. Hadders-Algra M

    (2008) Reduced variability in motor behaviour: an indicator of impaired cerebral connectivity?

    Early Human Development 84:787–789.

    https://doi.org/10.1016/j.earlhumdev.2008.09.002

    • PubMed
    • Google Scholar
38. 1. Hadders-Algra M

    (2018) Early human motor development: From variation to the ability to vary and adapt

    Neuroscience and Biobehavioral Reviews 90:411–427.

    https://doi.org/10.1016/j.neubiorev.2018.05.009

    • PubMed
    • Google Scholar
39. 1. Hart CB
    2. Giszter SF

    (2010) A neural basis for motor primitives in the spinal cord

    The Journal of Neuroscience 30:1322–1336.

    https://doi.org/10.1523/JNEUROSCI.5894-08.2010

    • PubMed
    • Google Scholar
40. 1. Hinnekens E
    2. Berret B
    3. Do M-C
    4. Teulier C

    (2020) Modularity underlying the performance of unusual locomotor tasks inspired by developmental milestones

    Journal of Neurophysiology 123:496–510.

    https://doi.org/10.1152/jn.00662.2019

    • PubMed
    • Google Scholar
41. 1. Hoyer PO

    (2004)

    Non-negative matrix factorization with sparseness constraints

    Journal of Machine Learning Research 5:1457–1469.

    • Google Scholar
42. 1. Ivanenko YP
    2. Poppele RE
    3. Lacquaniti F

    (2004) Five basic muscle activation patterns account for muscle activity during human locomotion

    The Journal of Physiology 556:267–282.

    https://doi.org/10.1113/jphysiol.2003.057174

    • PubMed
    • Google Scholar
43. 1. Ivanenko YP
    2. Dominici N
    3. Cappellini G
    4. Di Paolo A
    5. Giannini C
    6. Poppele RE
    7. Lacquaniti F

    (2013) Changes in the spinal segmental motor output for stepping during development from infant to adult

    The Journal of Neuroscience 33:3025–3036.

    https://doi.org/10.1523/JNEUROSCI.2722-12.2013

    • PubMed
    • Google Scholar
44. 1. Jeng SF
    2. Chen LC
    3. Yau KIT

    (2002) Kinematic analysis of kicking movements in preterm infants with very low birth weight and full-term infants

    Physical Therapy 82:148–159.

    https://doi.org/10.1093/ptj/82.2.148

    • PubMed
    • Google Scholar
45. 1. Kao MH
    2. Wright BD
    3. Doupe AJ

    (2008) Neurons in a forebrain nucleus required for vocal plasticity rapidly switch between precise firing and variable bursting depending on social context

    The Journal of Neuroscience 28:13232–13247.

    https://doi.org/10.1523/JNEUROSCI.2250-08.2008

    • PubMed
    • Google Scholar
46. 1. Kiehn O
    2. Kjaerulff O

    (1996) Spatiotemporal characteristics of 5-HT and dopamine-induced rhythmic hindlimb activity in the in vitro neonatal rat

    Journal of Neurophysiology 75:1472–1482.

    https://doi.org/10.1152/jn.1996.75.4.1472

    • PubMed
    • Google Scholar
47. 1. Klein DA
    2. Patino A
    3. Tresch MC

    (2010) Flexibility of motor pattern generation across stimulation conditions by the neonatal rat spinal cord

    Journal of Neurophysiology 103:1580–1590.

    https://doi.org/10.1152/jn.00961.2009

    • PubMed
    • Google Scholar
48. 1. Kolobe THA
    2. Fagg AH

    (2019) Robot reinforcement and error-based movement learning in infants with and without cerebral palsy

    Physical Therapy 99:677–688.

    https://doi.org/10.1093/ptj/pzz043

    • PubMed
    • Google Scholar
49. 1. Lacquaniti F
    2. Ivanenko YP
    3. Zago M

    (2012) Patterned control of human locomotion

    The Journal of Physiology 590:2189–2199.

    https://doi.org/10.1113/jphysiol.2011.215137

    • PubMed
    • Google Scholar
50. Conference

    1. Lapeyre M
    2. Ly O
    3. Oudeyer P-Y

    (2011) Maturational constraints for motor learning in high-dimensions: The case of biped walking

    2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011). pp. 707–714.

    https://doi.org/10.1109/Humanoids.2011.6100909

    • Google Scholar
51. 1. Lungarella M
    2. Berthouze L

    (2002) On the interplay between morphological, neural, and environmental dynamics: a robotic case study

    Adaptive Behavior 10:223–241.

    https://doi.org/10.1177/1059712302010003005

    • Google Scholar
52. 1. Mandelblat-Cerf Y
    2. Paz R
    3. Vaadia E

    (2009) Trial-to-trial variability of single cells in motor cortices is dynamically modified during visuomotor adaptation

    The Journal of Neuroscience 29:15053–15062.

    https://doi.org/10.1523/JNEUROSCI.3011-09.2009

    • PubMed
    • Google Scholar
53. 1. McGraw MB

    (1939) Swimming behavior of the human infant

    The Journal of Pediatrics 15:485–490.

    https://doi.org/10.1016/S0022-3476(39)80003-8

    • Google Scholar
54. 1. McGraw MB

    (1941) Development of neuro-muscular mechanisms as reflected in the crawling and creeping behavior of the human infant

    The Pedagogical Seminary and Journal of Genetic Psychology 58:83–111.

    https://doi.org/10.1080/08856559.1941.10534556

    • Google Scholar
55. 1. Morgan C
    2. Fetters L
    3. Adde L
    4. Badawi N
    5. Bancale A
    6. Boyd RN
    7. Chorna O
    8. Cioni G
    9. Damiano DL
    10. Darrah J
    11. de Vries LS
    12. Dusing S
    13. Einspieler C
    14. Eliasson A-C
    15. Ferriero D
    16. Fehlings D
    17. Forssberg H
    18. Gordon AM
    19. Greaves S
    20. Guzzetta A
    21. Hadders-Algra M
    22. Harbourne R
    23. Karlsson P
    24. Krumlinde-Sundholm L
    25. Latal B
    26. Loughran-Fowlds A
    27. Mak C
    28. Maitre N
    29. McIntyre S
    30. Mei C
    31. Morgan A
    32. Kakooza-Mwesige A
    33. Romeo DM
    34. Sanchez K
    35. Spittle A
    36. Shepherd R
    37. Thornton M
    38. Valentine J
    39. Ward R
    40. Whittingham K
    41. Zamany A
    42. Novak I

    (2021) Early intervention for children aged 0 to 2 years with or at high risk of cerebral palsy

    JAMA Pediatrics 175:846.

    https://doi.org/10.1001/jamapediatrics.2021.0878

    • Google Scholar
56. 1. Müller J
    2. Müller S
    3. Baur H
    4. Mayer F

    (2013) Intra-individual gait speed variability in healthy children aged 1-15 years

    Gait & Posture 38:631–636.

    https://doi.org/10.1016/j.gaitpost.2013.02.011

    • PubMed
    • Google Scholar
57. 1. Mussa-Ivaldi FA
    2. Giszter SF
    3. Bizzi E

    (1994) Linear combinations of primitives in vertebrate motor control

    PNAS 91:7534–7538.

    https://doi.org/10.1073/pnas.91.16.7534

    • Google Scholar
58. 1. Neptune RR
    2. Clark DJ
    3. Kautz SA

    (2009) Modular control of human walking: A simulation study

    Journal of Biomechanics 42:1282–1287.

    https://doi.org/10.1016/j.jbiomech.2009.03.009

    • PubMed
    • Google Scholar
59. 1. Newell KM
    2. Van Emmerik REA
    3. McDonald PV

    (1989) Biomechanical constraints and action theory

    Human Movement Science 8:403–409.

    https://doi.org/10.1016/0167-9457(89)90045-6

    • Google Scholar
60. 1. Ossmy O
    2. Adolph KE

    (2020) Real-time assembly of coordination patterns in human infants

    Current Biology 30:4553–4562.

    https://doi.org/10.1016/j.cub.2020.08.073

    • PubMed
    • Google Scholar
61. 1. Overduin SA
    2. d’Avella A
    3. Carmena JM
    4. Bizzi E

    (2012) Microstimulation activates a handful of muscle synergies

    Neuron 76:1071–1077.

    https://doi.org/10.1016/j.neuron.2012.10.018

    • PubMed
    • Google Scholar
62. 1. Overduin SA
    2. d’Avella A
    3. Roh J
    4. Carmena JM
    5. Bizzi E

    (2015) Representation of muscle synergies in the primate brain

    The Journal of Neuroscience 35:12615–12624.

    https://doi.org/10.1523/JNEUROSCI.4302-14.2015

    • PubMed
    • Google Scholar
63. 1. Piek JP

    (2002) The role of variability in early motor development

    Infant Behavior and Development 25:452–465.

    https://doi.org/10.1016/S0163-6383(02)00145-5

    • Google Scholar
64. Book

    1. Robinson SR
    2. Kleven GA

    (2005)

    Learning to move before birth

    In: Hopkins B, Johnson SP, editors. Prenatal development of postnatal functions. Praeger Publishers. pp. 131–175.

    • Google Scholar
65. 1. Robinson SR

    (2015) Spinal mediation of motor learning and memory in the rat fetus

    Developmental Psychobiology 57:421–434.

    https://doi.org/10.1002/dev.21277

    • PubMed
    • Google Scholar
66. 1. Roh J
    2. Cheung VCK
    3. Bizzi E

    (2011) Modules in the brain stem and spinal cord underlying motor behaviors

    Journal of Neurophysiology 106:1363–1378.

    https://doi.org/10.1152/jn.00842.2010

    • PubMed
    • Google Scholar
67. 1. Sargent B
    2. Havens KL
    3. Wisnowski JL
    4. Wu T-W
    5. Kubo M
    6. Fetters L

    (2020) In-home kicking-activated mobile task to motivate selective motor control of infants at high risk of cerebral palsy: A feasibility study

    Physical Therapy 100:2217–2226.

    https://doi.org/10.1093/ptj/pzaa174

    • PubMed
    • Google Scholar
68. 1. Schaal S

    (2006) Dynamic movement primitives -a framework for motor control in humans and humanoid robotics

    Adaptive Motion of Animals and Machines 1:261–280.

    https://doi.org/10.1007/4-431-31381-8

    • Google Scholar
69. 1. Selinger JC
    2. O’Connor SM
    3. Wong JD
    4. Donelan JM

    (2015) Humans can continuously optimize energetic cost during walking

    Current Biology 25:2452–2456.

    https://doi.org/10.1016/j.cub.2015.08.016

    • Google Scholar
70. 1. Siekerman K
    2. Barbu-Roth M
    3. Anderson DI
    4. Donnelly A
    5. Goffinet F
    6. Teulier C

    (2015) Treadmill stimulation improves newborn stepping

    Developmental Psychobiology 57:247–254.

    https://doi.org/10.1002/dev.21270

    • PubMed
    • Google Scholar
71. 1. Spencer JP
    2. Thelen E

    (2000) Spatially specific changes in infants’ muscle coactivity as they learn to reach

    Infancy 1:275–302.

    https://doi.org/10.1207/S15327078IN0103_1

    • PubMed
    • Google Scholar
72. 1. Sutton RS
    2. Barto AG

    (1998) Reinforcement Learning: An Introduction

    IEEE Transactions on Neural Networks 9:1054.

    https://doi.org/10.1109/TNN.1998.712192

    • Google Scholar
73. 1. Sylos-Labini F
    2. La Scaleia V
    3. Cappellini G
    4. Fabiano A
    5. Picone S
    6. Keshishian ES
    7. Zhvansky DS
    8. Paolillo P
    9. Solopova IA
    10. d’Avella A
    11. Ivanenko Y
    12. Lacquaniti F

    (2020) Distinct locomotor precursors in newborn babies

    PNAS 117:9604–9612.

    https://doi.org/10.1073/pnas.1920984117

    • Google Scholar
74. 1. Sylos-Labini F
    2. La Scaleia V
    3. Cappellini G
    4. Dewolf A
    5. Fabiano A
    6. Solopova IA
    7. Mondì V
    8. Ivanenko Y
    9. Lacquaniti F

    (2022) Complexity of modular neuromuscular control increases and variability decreases during human locomotor development

    Communications Biology 5:1256.

    https://doi.org/10.1038/s42003-022-04225-8

    • PubMed
    • Google Scholar
75. 1. Teulier C
    2. Smith BA
    3. Kubo M
    4. Chang CL
    5. Moerchen V
    6. Murazko K
    7. Ulrich BD

    (2009) Stepping responses of infants with myelomeningocele when supported on a motorized treadmill

    Physical Therapy 89:60–72.

    https://doi.org/10.2522/ptj.20080120

    • PubMed
    • Google Scholar
76. 1. Teulier C
    2. Sansom JK
    3. Muraszko K
    4. Ulrich BD

    (2012) Longitudinal changes in muscle activity during infants’ treadmill stepping

    Journal of Neurophysiology 108:853–862.

    https://doi.org/10.1152/jn.01037.2011

    • PubMed
    • Google Scholar
77. 1. Thelen E
    2. Bradshaw G
    3. Ward JA

    (1981) Spontaneous kicking in month-old infants: manifestation of a human central locomotor program

    Behavioral and Neural Biology 32:45–53.

    https://doi.org/10.1016/s0163-1047(81)90257-0

    • PubMed
    • Google Scholar
78. 1. Thelen E
    2. Fisher DM

    (1982) Newborn stepping: An explanation for a “disappearing” reflex

    Developmental Psychology 18:760–775.

    https://doi.org/10.1037/0012-1649.18.5.760

    • Google Scholar
79. 1. Thelen E
    2. Fisher DM

    (1983a) The organization of spontaneous leg movements in newborn infants

    Journal of Motor Behavior 15:353–377.

    https://doi.org/10.1080/00222895.1983.10735305

    • PubMed
    • Google Scholar
80. 1. Thelen E
    2. Ridley-Johnson R
    3. Fisher DM

    (1983b) Shifting patterns of bilateral coordination and lateral dominance in the leg movements of young infants

    Developmental Psychobiology 16:29–46.

    https://doi.org/10.1002/dev.420160105

    • PubMed
    • Google Scholar
81. 1. Torres-Oviedo G
    2. Ting LH

    (2010) Subject-specific muscle synergies in human balance control are consistent across different biomechanical contexts

    Journal of Neurophysiology 103:3084–3098.

    https://doi.org/10.1152/jn.00960.2009

    • PubMed
    • Google Scholar
82. 1. Tresch MC
    2. Saltiel P
    3. Bizzi E

    (1999) The construction of movement by the spinal cord

    Nature Neuroscience 2:162–167.

    https://doi.org/10.1038/5721

    • PubMed
    • Google Scholar
83. 1. Ulrich BD

    (2010) Opportunities for early intervention based on theory, basic neuroscience, and clinical science

    Physical Therapy 90:1868–1880.

    https://doi.org/10.2522/ptj.20100040

    • PubMed
    • Google Scholar
84. 1. Vaal J
    2. van Soest AJ
    3. Hopkins B
    4. Sie LT
    5. van der Knaap MS

    (2000) Development of spontaneous leg movements in infants with and without periventricular leukomalacia

    Experimental Brain Research 135:94–105.

    https://doi.org/10.1007/s002210000508

    • PubMed
    • Google Scholar
85. Book

    1. Valero-Cuevas FJ

    (2009) A mathematical approach to the mechanical capabilities of limbs and fingers

    In: Sternad D, editors. Progress in Motor Control. Boston, MA: Springer. pp. 619–633.

    https://doi.org/10.1007/978-0-387-77064-2

    • Google Scholar
86. 1. Vinay L
    2. Brocard F
    3. Clarac F
    4. Norreel JC
    5. Pearlstein E
    6. Pflieger JF

    (2002) Development of posture and locomotion: an interplay of endogenously generated activities and neurotrophic actions by descending pathways

    Brain Research. Brain Research Reviews 40:118–129.

    https://doi.org/10.1016/s0165-0173(02)00195-9

    • PubMed
    • Google Scholar
87. 1. Yang Q
    2. Logan D
    3. Giszter SF

    (2019) Motor primitives are determined in early development and are then robustly conserved into adulthood

    PNAS 116:12025–12034.

    https://doi.org/10.1073/pnas.1821455116

    • PubMed
    • Google Scholar

Author details

1. Elodie Hinnekens

   1. Université Paris-Saclay, CIAMS, Orsay, France
   2. Université d'Orléans, CIAMS, Orléans, France

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration

   For correspondence

   elodie.hinnekens@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0649-0779

2. Marianne Barbu-Roth

   Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, Paris, France

   Contribution

   Conceptualization, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

3. Manh-Cuong Do

   1. Université Paris-Saclay, CIAMS, Orsay, France
   2. Université d'Orléans, CIAMS, Orléans, France

   Contribution

   Conceptualization, Resources, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

4. Bastien Berret

   1. Université Paris-Saclay, CIAMS, Orsay, France
   2. Université d'Orléans, CIAMS, Orléans, France
   3. Institut Universitaire de France, Paris, France

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Caroline Teulier

   Competing interests

   No competing interests declared

5. Caroline Teulier

   1. Université Paris-Saclay, CIAMS, Orsay, France
   2. Université d'Orléans, CIAMS, Orléans, France

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Investigation, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Bastien Berret

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4400-783X

• 826

  views

• 154

  downloads

• 4

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 4

  citations for umbrella DOI https://doi.org/10.7554/eLife.87463