Editorial: Modularity in motor control: from muscle synergies to cognitive action representation

d’Avella, Andrea; Giese, Martin A.; Ivanenko, Yuri P.; Schack, Thomas; Flash, Tamar

doi:10.3389/fncom.2015.00126

Cited by 56 publications

(65 citation statements)

References 54 publications

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“…One concerns the question of how to control the movement of an individual leg and the other is how to couple the movement of the different legs. For both problems modularization appears to be a sensible solution (D'Avella et al 2015;Hassabis et al, 2017).…”

Section: Decentralization As a Central Control Principlementioning

confidence: 99%

“…Motor control in animals deals with the control of a very high number of muscles (or degrees of freedom) that have to be controlled in a coordinated way in order to achieve complex behaviors. One key principle that allows animals to deal with such a complex control problem is modularization (D'Avella et al 2015;Flash & Hochner, 2005;Mussa-Ivaldi 1999;Botvinick 2008), i.e. breaking the overall complexity of the control system down on a structural level (Binder et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Control of Insect Walking – a simple neural network explains a wide range of behavioral and neurophysiological results

Schilling

Cruse

2019

Preprint

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AbstractControl of walking with six or more legs in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Generally, solutions are proposed that rely on (sensory-modulated) CPGs, mainly based on data from neurophysiological studies. Here, we are introducing a sensor based controller operating on artificial neurons, being applied to a (simulated) hexapod robot with a morphology adapted to Carausius morosus. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a large range of behaviors – slow and fast walking, forward and backward walking, negotiation of curves and walking on a treadmill with various treatment of individual legs. This approach can as well account for these neurophysiological results without relying on explicit CPG-like structures, but can be complemented with these for very fast walking.

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Section: Decentralization As a Central Control Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized Control of Insect Walking – a simple neural network explains a wide range of behavioral and neurophysiological results

Schilling

Cruse

2019

Preprint

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“…The coordination of muscles and joints that accompanies movement requires multiple degree of freedoms (DoFs). This results a high level of complexity and dimensionality [1]. A possible explanation to this problem considers the notion that the CNS constructs a movement as a combination of small groups of muscles (synergies) that act in harmony with each other, thus reducing the dimensionality of the problem.…”

Section: Muscle Synergymentioning

confidence: 99%

Evaluation of matrix factorisation approaches for muscle synergy extraction

Ebied¹,

Kinney‐Lang²,

Spyrou³

et al. 2018

Medical Engineering & Physics

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The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.

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“…It has been hypothesized that in neuromuscular control the nervous system relies on muscle synergies (i.e., motor modules), each of which is constituted by two components: The muscle weightings and their temporal activation profiles (Cheung et al, 2009). The motor modules are functional structures related to specific motor patterns, defined as coordinated patterns of muscle activity that flexibly combine to produce functional motor behaviors (Bizzi and Cheung, 2013; Berger and d'Avella, 2014; Ting et al, 2015; d'Avella et al, 2015). The interpretation of those functional structures is, however, still under debate; some consider them as fixed co-excited groups of muscles that contribute toward specific biomechanical function (Ting and Macpherson, 2005; Routson et al, 2014), while others view them as having developed due to optimal control (de Rugy et al, 2013; Ting et al, 2015) or emerging as the result of biomechanical constraints (Kutch and Valero-Cuevas, 2012; Ting et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Are Modular Activations Altered in Lower Limb Muscles of Persons with Multiple Sclerosis during Walking? Evidence from Muscle Synergies and Biomechanical Analysis

Lencioni

Jonsdottir

Cattaneo

et al. 2016

Front. Hum. Neurosci.

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Background: Persons with Multiple Sclerosis frequently have gait deficits that lead to diminished activities of daily living. Identification of motoneuron activity patterns may elucidate new insight into impaired locomotor coordination and underlying neural systems. The aim of the present study was to investigate muscle synergies, identified by motor modules and their activation profiles, in persons with Multiple Sclerosis (PwMS) during walking compared to those of healthy subjects (HS), as well as, exploring relationship of muscle synergies with walking ability of PwMS.Methods: Seventeen PwMS walked at their natural speed while 12 HS walked at slower than their natural speeds in order to provide normative gait values at matched speeds (spatio-temporal, kinematic, and kinetic parameters and electromyography signals). Non-negative matrix factorization was used to identify muscle synergies from eight muscles. Pearson's correlation coefficient was used to evaluate the similarity of motor modules between PwMS and HS. To assess differences in module activations, each module's activation timing was integrated over 100% of gait cycle and the activation percentage was computed in six phases.Results: Fifty-nine% of PwMS and 58% of HS had 4 modules while the remaining of both populations had 3 modules. Module 2 (related to soleus, medial, and lateral gastrocnemius primarily involved in mid and terminal stance) and Module 3 (related to tibialis anterior and rectus femoris primarily involved in early stance, and early and late swing) were comparable across all subjects regardless of synergies number. PwMS had shorter stride length, longer double support phase and push off deficit with respect to HS (p < 0.05). The alterations of activation timing profiles of specific modules in PwMS were associated with their walking deficits (e.g., the reduction of Module 2 activation percentage index in terminal stance, PwMS 35.55 ± 13.23 vs. HS 50.51 ± 9.13% p < 0.05, and the push off deficit, PwMS 0.181 ± 0.136 vs. HS 0.291 ± 0.062 w/kg p < 0.05).Conclusion: During gait PwMS have synergies numbers similar to healthy persons. Their neurological deficit alters modular control through modifications of the timing activation profiles rather than module composition. These changes were associated with their main walking impairment, muscle weakness, and prolonged double support.

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Editorial: Modularity in motor control: from muscle synergies to cognitive action representation

Cited by 56 publications

References 54 publications

Decentralized Control of Insect Walking – a simple neural network explains a wide range of behavioral and neurophysiological results

Decentralized Control of Insect Walking – a simple neural network explains a wide range of behavioral and neurophysiological results

Evaluation of matrix factorisation approaches for muscle synergy extraction

Are Modular Activations Altered in Lower Limb Muscles of Persons with Multiple Sclerosis during Walking? Evidence from Muscle Synergies and Biomechanical Analysis

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