Mastering a rich repertoire of motor behaviors, as humans and other animals do, is
a surprising and still a poorly understood outcome of evolution, development, and
learning. Many degrees-of-freedom, non-linear dynamics, and sensory delays provide
formidable challenges for controlling even simple actions. Modularity as a functional
element, both structural and computational, of a control architecture might be the
key organizational principle that the central nervous system employs for achieving
versatility and adaptability in motor control. Recent investigations of muscle synergies,
motor primitives, compositionality, basic action concepts, and related work in machine
learning have contributed, at different levels, to advance our understanding of the
modular architecture underlying rich motor behaviors.
However, the existence and nature of the modules comprising the control architecture
is far from settled. For instance, regularity and low-dimensionality of the motor
output are often taken as an indication of modularity but they could simply be a byproduct
of optimization and task constraints. Moreover, what are the relationships between
modules at different levels, such as muscle synergies, kinematic invariants, and basic
action concepts?
One important reason for the new interest in understanding modularity in motor control
from different perspectives is the impressive development in cognitive robotics. In
comparison to animals and humans, the motor skills of today's best robots are limited
and inflexible. However, robot technology is maturing to the point at which it can
start approximating a reasonable spectrum of different perceptual, cognitive, and
motor capabilities. These advances allow researchers to explore how these motor, sensory,
and cognitive functions might be integrated into meaningful architectures and to test
their functional limits. Such systems provide a new test bed to explore different
concepts of modularity and to experimentally investigate possible interactions between
motor and cognitive processes.
Thus, the goal of this Research Topic is to review, compare, and debate theoretical
and experimental studies of the modular organization of the motor control system at
different levels. By bringing together researchers seeking to understand the building
blocks of coordinating many muscles, planning endpoint and joint trajectories, and
representing motor and behavioral actions in memory we aim at promoting new interactions
between often disconnected research areas and approaches and providing a broad perspective
on the notion of modularity in motor control.
Reviews and perspectives
A number of review articles present and discuss available evidence, conceptual frameworks,
and fundamental questions concerning modularity in motor control. These cover a range
of issues such as the effective dimensionality, movement invariants, neural underpinnings,
evolution, motor learning, and recovery of motor function.
Lacquaniti et al. (2013) provides a comprehensive review of evolutionary and developmental
modules. These authors focus on modular control of locomotion to argue that the building
blocks used to construct different locomotor behaviors are similar across several
animal species, presumably related to ancestral neural networks of command. The authors
present evidence that modular units of development are highly preserved and recombined
during evolution.
In a thought-provoking review article, Duysens et al. (2013) argue that there is large
overlap between the notions on modules and the older concepts of reflexes. They reason
that facilitation of the flexor synergy at the end of the stance phase is linked to
the activation of circuitry that is responsible for the generation of locomotor patterns
(CPG, “central pattern generator”). More specifically, it is suggested that the responses
in that period relate to the activation of a flexor burst generator. The latter structure
forms the core of a new asymmetric model of the CPG. Beloozerova et al. (2013) review
data on the differential controls for the shoulder, elbow, and wrist that are used
by populations of neurons in the thalamo-cortical network. It is one of manifestations
of a modular organization of control for locomotion. The authors hypothesize that
this contributes to an effective control of a global limb parameter, the length of
the stride, which results in a great reduction in variability of paw placement during
accurate stepping.
Santello et al. (2013) propose a theoretical framework to reconcile important and
still debated concepts such as the definitions of “fixed” vs. “flexible” synergies
and mechanisms underlying the combination of synergies for hand control. d'Avella
and Lacquaniti (2013) review recent results from the analysis of reaching muscle patterns
supporting a control strategy consisted in the sequencing of time-varying muscle synergies.
Alessandro et al. (2013b) review the works related to muscle synergies in neuroscience
and control engineering and provide an overview of the methods that have been employed
to test the validity of the control scheme. Specifically, the authors suggest that
to assess the functional role of muscle synergies, synergy extraction methods should
explicitly take into account task execution variables. Bizzi and Cheung (2013) address
two critical questions: the explicit encoding of muscle synergies in the nervous system,
and how muscle synergies simplify movement production and motor learning.
Another important field of research is the outcome of interventions in neurological
disorders with motor deficits. Uncovering a common underlying neural framework for
the modular control of movements and its dysfunction represents an interesting avenue
for future work. Casadio et al. (2013) review the state of the art of computational
models for neuromotor recovery from stroke through exercise, and their implications
for treatment. The review specifically covers models of recovery at central, functional
and muscle synergy level. Ivanenko et al. (2013) review various examples of adaptation
of locomotor patterns in patients and discuss the findings in a general context of
compensatory gait mechanisms, spatiotemporal architecture, and modularity of the locomotor
program. Such investigations may have important implications related to the construction
of gait rehabilitation technology. Further research needs to clarify whether plasticity
in muscle patterns originates from sharing common modules or by creating new muscle
synergies and whether the rehabilitation programs may benefit from revitalizing the
modules underlying motor behaviors.
Muscle synergies
Amongst the original research articles, a large group of contributions is dedicated
to the modular organization of multi-muscle activity across different motor tasks.
It has been hypothesized that the nervous system simplifies muscle control through
modularity, using neural patterns to activate muscles in groups called synergies.
An important example of ongoing debate is the current discussion of the critical aspects
and organization of muscle synergies. de Rugy et al. (2013) argue that the usefulness
of muscle synergies as a control principle should be evaluated in terms of errors
produced and, using data from a force-aiming task in two dimensions, illustrate through
simulation how synergy decomposition inevitably introduces substantial task space
errors. They also show that the number of synergies required to approximate the optimal
muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality,
which indicates that the capacity of synergy decomposition to explain behavior depends
critically on the scope of the original database. Steele et al. (2013) present evidence
that the number and choice of muscles impact the results of muscle synergy analyses.
Thus, researchers should be cautious in evaluating muscle synergies when EMG is measured
from a small subset of muscles.
Delis et al. (2013a,b) stress the effectiveness of the decoding metric in systematically
assessing muscle synergy decompositions in task space and the functional role of trial-to-trial
correlations between synergy activations. The results of Chiovetto et al. (2013) support
the notion that each EMG decomposition provides a set of well-interpretable muscle
synergies, identifying reduction of dimensionality in different aspects of the movements.
Borzelli et al. (2013) test whether the CNS generates forces by minimum effort recruitment
of either individual muscles or muscle synergies during the generation of isometric
forces at the hand. The minimum effort recruitment of synergies predicts the observed
muscle patterns better than the minimum effort recruitment of individual muscles.
Russo et al. (2014) compare the torques acting at four arm joints during fast reaching
movements in different directions and show that muscle pattern dimensionalities are
higher than torques dimensionalities. They argue that this is necessary to overcome
the non-linearities of the musculoskeletal system and to flexibly generate endpoint
trajectories with simple kinematic features using a limited number of building blocks.
In the context of direction-specific recruitment of muscle synergies, Gentner et al.
(2013) investigate adaptation to a visuomotor rotation of a virtual target displacement
and show that the structure of muscle synergies is preserved, suggesting that changes
in muscle patterns are obtained by rotating the directional tuning of the synergy
recruitment. Bengoetxea et al. (2014a,b) employ a dynamic recurrent neural network
(DRNN) and principal component analysis of EMG activity during discrete and rhythmic
arm movements. The authors discuss consistent patterns of muscle groupings in the
context of their functional organization for controlling orthogonal movement directions.
Berger and d'Avella (2014) recorded EMG activity and isometric hand forces during
a force-aiming task in a virtual environment. In contrast to de Rugy et al. (2013),
they show that muscle synergies can be used to generate target forces in multiple
directions with the same accuracy achieved using individual muscles. Strikingly, human
subjects are able to perform the task immediately after switching from force-control
to EMG-control and synergy-control, suggesting that muscle synergies provide an effective
strategy for motor coordination.
Whether muscle synergies are shared across tasks or they are task-specific is another
debated aspect of modularity. Chvatal and Ting (2013) compare muscle synergies during
multidirectional support-surface perturbations during standing and walking, as well
as unperturbed walking. They find both shared and task-specific muscle synergies,
suggesting that differences in muscle synergies across conditions reflect differences
in the biomechanical demands of the tasks and that muscle synergies may define a repertoire
of biomechanical subtasks recruited according to task-level goals. Frere and Hug (2012)
demonstrate that the muscle synergies are consistent across experienced gymnasts,
even during a skilled motor task that requires learning. De Marchis et al. (2013)
investigate muscle synergies during pedaling in humans. Additional modules are identified
when visual feedback about mechanical effectiveness is available and the structure
of the identified modules is found similar to that extracted in other studies of human
walking, confirming the existence of shared and task specific muscle synergies. Finally,
Hart and Giszter (2013) present a method that uses point process statistics to discriminate
the forms of synergies in motor pattern data. According to this method, frog and rat
EMG data are most consistent with synchronous synergy models, supporting separated
control of rhythm and pattern of motor primitives.
Motor primitives at the kinematic level
A number of contributions aim at understanding motor primitives at the kinematic level.
Zelman et al. (2013) explore whether different octopus arm movements are built up
of elementary kinematic units by decomposing surfaces, representing curvature, and
torsion values of the paths of points along the arm, into a weighted combination of
2D Gaussian functions, considered as motion primitives at the kinematic level of octopus
arm movements. Endres et al. (2013a) investigate the endpoint trajectories of human
movements (sign language) that are characterized by the power laws linking velocity
and curvature. The parameters of these power laws are exploited for the unsupervised
segmentation of actions into movement primitives. Sternad et al. (2013) propose that
control of sensorimotor behavior may utilize dynamic primitives. Their results clearly
indicate a gradual transition between discrete and rhythmic arm movements, supporting
the proposal that representation is based on primitives rather than on veridical internal
models. Boyer et al. (2013) investigate interactions between the auditory and motor
systems to uncover different modular neural processes involved in the multisensory
and motor representations of targets in goal-directed movements and corresponding
reference frames for each sensory modality. Racz and Valero-Cuevas (2013) suggest
that the similar nature of control actions across time scales in both task-relevant
and task-irrelevant spaces points to a level of modularity not previously recognized
in motor tasks. Hogan and Sternad (2013) propose that the spectacular performance
of a wide range of upper- and lower-limb behaviors arises from encoding motor commands
in terms of three classes of dynamic primitives: submovements, oscillations, and mechanical
impedances. They present some methods for addressing the challenges posed by the experimental
identification of these dynamic primitives and consider the implications of this theoretical
framework for locomotor rehabilitation.
Neural substrates
Another exciting area explored in this Research Topic is potential neural substrates
for modularity in motor control and action representation. Takei and Seki (2013) argue
about synaptic and functional linkage between spinal interneurons and the organization
of hand-muscle synergies. Abeles et al. (2013) discuss the compositional structure
of hand movements by analyzing and modeling neural and behavioral data obtained from
experiments where monkeys performed scribbling movements. A classification of the
neural data employing a hidden Markov model shows a coincidence of the neural states
with the behavioral categories of movement segmentations that are primarily parabolic
in shape. Overduin et al. (2014) investigate whether muscle synergies evoked by intracortical
microstimulation (ICMS) in rhesus macaques are similarly encoded by nearby motor cortical
units during object reach, grasp, and carry movements. They find that the synergy
most strongly evoked at an ICMS site matches the synergy most strongly encoded by
proximal units more often than expected by chance. The results suggest a common neural
substrate for microstimulation-evoked motor responses and for the generation of muscle
patterns during natural behaviors. Krouchev and Drew (2013) describe a modular organization
of the locomotor step cycle in the cat in which a number of sparse synergies are activated
sequentially during unobstructed locomotion and during voluntary gait modifications.
The authors argue that the changes in phase and magnitude of a finite number of muscle
synergies could be produced by changes in the activity of neurons in the motor cortex.
Mokienko et al. (2013) study motor imagery of grasping movements and corresponding
neural underpinnings in brain-computer interface trained human subjects.
Models
A number of modeling papers address different aspects of modularity. In a multi-directional
reaching task simulated with a musculoskeletal model of the human arm, Ruckert and
d'Avella (2013) propose a movement primitive representation that employs parametrized
basis functions, which combines the benefits of muscle synergies and dynamic movement
primitives, and show how movement primitives can be used to learn appropriate muscle
excitation patterns and to generalize effectively to new reaching skills. Sartori
et al. (2013) use a Gaussian-shaped impulsive excitation curves or primitives as input
drive for large musculoskeletal models across different human locomotion tasks. Alessandro
et al. (2013a) examine the feasibility of controlling non-linear dynamical systems
by linear combinations of a small set of torque profiles or motor synergies and suggest
that in order to realize an effective and low-dimensional controller, synergies should
embed features of both the desired tasks and the system dynamics.
Significant progress has been made with respect to some fundamental questions concerning
optimization of control architectures and motor learning. Rückert et al. (2012) propose
a movement primitive representation based on probabilistic inference in learned graphical
models with properties that comply with salient features of biological movement control.
In simulations of a complex 4-link balancing task, they show that movement primitives
facilitate learning and lead to better generalization. Endres et al. (2013b) address
the issue of the selection of the parameters of movement primitive models or the model
type and propose an approach based on a Laplace approximation to the posterior distribution
of the parameters of a given blind source separation model. They validate the approach
on simulated data and on human gait data, finding that an anechoic mixture model with
a temporal smoothness constraint on the sources can best account for the data. Kuppuswamy
and Harris (2014) investigate whether muscle synergies can reduce the state-space
dimensionality while maintaining task control. Based on the observation that constraining
the control input to a weighted combination of temporal muscle synergies also constrains
the dynamic behavior of a system in a trajectory-specific manner, they show that smooth
straight-line Cartesian trajectories with bell-shaped velocity profiles emerged as
the optima for the reaching task and that trajectory and synergy specific dimensionality
reduction results from muscle synergy control. Hayashibe and Shimoda (2014) aim at
identifying a modular control architecture realizing adaptability and optimality without
prior knowledge of system dynamics. They propose a novel motor control paradigm based
on tacit learning with task space feedback. The proposed paradigm can optimize solutions
for reaching with a three-joint, planar biomechanical model, acquiring motor synergy,
and finding energy efficient solutions for different load conditions.
A few contributions further examine the usage of neural networks. Schilling et al.
(2013) demonstrate a solution for the selection and sequencing of different (attractor)
states required to control different behaviors of a hexapod walker as forward walking
at different speeds, backward walking, as well as negotiation of tight curves. The
proposed control architecture of a recurrent neural network is characterized by different
types of modules being arranged in layers and columns, and can also be considered
as a holistic system showing emergent properties which cannot be attributed to a specific
module. Hoellinger et al. (2013) describe the use of a DRNN mimicking the natural
oscillatory behavior of human locomotion for reproducing the planar covariation rule
in both legs at different walking speeds. This emerging property in the artificial
neural networks resonates with recent advances in neurophysiology of inhibitory neurons
that are involved in central nervous system oscillatory activities. The main message
of this study is that this type of DRNN may offer a useful model of physiological
central pattern generators for the purpose of gaining insights in basic research and
developing clinical applications.
Ehrenfeld et al. (2013) address the question of how the brain maintains a probabilistic
body state estimate over time from a modeling perspective. The results showed that
the neural estimates can detect and decrease the impact of false sensory information,
can propagate conflicting information across modules, and can improve overall estimation
accuracy due to additional module interactions. Finally, Tagliabue and Mcintyre (2014)
review different formulations of concurrent models for sensory integration and propose
a modular approach in which the overall behavior is built by computing multiple concurrent
comparisons carried out simultaneously in a number of different reference frames.
Robotics
Findings in biological research concerning a modular control hierarchy, which combines
movement/motor primitives into complex and natural movements, inspire engineers in
the quest for adaptive and skillful control for robots. Neumann et al. (2014) present
a unified approach for learning a modular control architecture, introducing new policy
search algorithms that are based on information-theoretic principles and are able
to learn to select, adapt, and sequence the building blocks to compose more complex
behaviors. The authors summarize their experiments for learning modular control architectures
in simulation and with real robots. Waegeman et al. (2013) propose a modular architecture
with control primitives (MACOP) which uses a set of controllers, where each controller
becomes specialized in a subregion of its joint and task-space. The authors evaluate
MACOP on a numerical model of a robot arm by training it to generate desired trajectories
and show how MACOP compensates for the dynamic effects caused by a fixed control rate
and the inertia of the robot. Nakajima et al. (2013) explore the idea that control,
which is conventionally thought to be handled by the brain or a controller, can partially
be outsourced to the physical body and the interaction with the environment. By using
a soft robotic arm inspired by the octopus they show in a number of experiments how
control is partially incorporated into the physical arm's dynamics and how the arm's
dynamics can be exploited to approximate non-linear dynamical systems. Spröwitz et
al. (2014) implement kinematic primitives for walking and trotting gaits of a quadruped
robot and show that a very low complexity of modular, rhythmic, feed-forward motor
control is sufficient for level-ground locomotion in combination with passive compliant
legged hardware.
Intermittent control
Evidence for intermittency in human motor control has been repeatedly observed in
the neural control of movement literature and it has been discussed in this Research
Topic in the context of the modular organization of the motor control system. Karniel
(2013) focuses on an area in which intermittent control has not yet been thoroughly
considered, with respect to the structure of muscle synergies. He presents the minimum
transition hypothesis and its predictions with regard to the structure of muscle synergies.
D'Andola et al. (2013) demonstrate that that the control of interceptive movements
(catching a flying ball) relies on a combination of reactive and predictive processes
through the intermittent recruitment of time-varying muscle synergies. van de Kamp
et al. (2013) explore modular organization in whole body control architecture within
the intermittent control paradigm with an intermittent interval of around 0.5 s. The
authors suggest that parallel sensory input converges to a serial, single channel
process involving planning, selection, and temporal inhibition of alternative responses
prior to low dimensional motor output and may underlie the flexibility of human control.
Such studies may have important implications with respect to the design of brain machine
interfaces and human robot interaction.
Action representation
The final theme we have identified in the contributions centers on the modular organization
and interaction between motor and cognitive processes. Land et al. (2013) explore
the links between cognitive and biomechanical levels of motor control in order to
understand the extent to which the output at a kinematic level is governed by representations
at a cognitive level of motor control. The authors apply a new spatio-temporal decomposition
method for assessing memory structures underlying complex actions in order to investigate
the overlap between the structure of motor representations in memory and their corresponding
kinematic structures.
Taken together, this Research Topic demonstrates the impressive breadth of research
currently being undertaken on modularity in motor control.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.