Identification of synergies by optimization of trajectory tracking tasks

Alessandro, Cristiano; Nori, Francesco

doi:10.1109/biorob.2012.6290701

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…As discussed in section 3.4, our work suggests that synergies can be obtained by solving well defined control problems. Similar ideas have already been proposed (Chhabra and Jacobs, 2008; Todorov, 2009; Alessandro and Nori, 2012; Thomas and Barto, 2012). However, these studies do not investigate which class of problems are best suited for this purpose.…”

Section: Discussionsupporting

confidence: 74%

A computational analysis of motor synergies by dynamic response decomposition

Alessandro

Carbajal

d’Avella

2014

Front. Comput. Neurosci.

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Analyses of experimental data acquired from humans and other vertebrates have suggested that motor commands may emerge from the combination of a limited set of modules. While many studies have focused on physiological aspects of this modularity, in this paper we propose an investigation of its theoretical foundations. We consider the problem of controlling a planar kinematic chain, and we restrict the admissible actuations to linear combinations of a small set of torque profiles (i.e., motor synergies). This scheme is equivalent to the time-varying synergy model, and it is formalized by means of the dynamic response decomposition (DRD). DRD is a general method to generate open-loop controllers for a dynamical system to solve desired tasks, and it can also be used to synthesize effective motor synergies. We show that a control architecture based on synergies can greatly reduce the dimensionality of the control problem, while keeping a good performance level. Our results 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. These characteristics can be achieved by defining synergies as solutions to a representative set of task instances. The required number of synergies increases with the complexity of the desired tasks. However, a possible strategy to keep the number of synergies low is to construct solutions to complex tasks by concatenating synergy-based actuations associated to simple point-to-point movements, with a limited loss of performance. Ultimately, this work supports the feasibility of controlling a non-linear dynamical systems by linear combinations of basic actuations, and illustrates the fundamental relationship between synergies, desired tasks and system dynamics.

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Section: Discussionsupporting

confidence: 74%

A computational analysis of motor synergies by dynamic response decomposition

Alessandro

Carbajal

d’Avella

2014

Front. Comput. Neurosci.

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“…For example, the quadriceps muscles VL and VM have very similar contributions to task performance with each muscle producing a similar extension torque at the knee (9)(10)(11); in rats, which have knees that can move in both flexion/extension and pronation/supination, VM and VL produce similar actions along all degrees of freedom (11). Consistent with the proposal that covariation patterns reflect muscles' contribution to task performance (7,(12)(13)(14)(15), the activations of VM and VL are strongly correlated (15)(16)(17)(18), suggesting that the CNS might control VM and VL as a single functional unit to simplify the achievement of task goals (19).…”

supporting

confidence: 65%

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Alessandro

Barroso

Prashara

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Many studies have demonstrated covariation between muscle activations during behavior, suggesting that muscles are not controlled independently. According to one common proposal, this covariation reflects simplification of task performance by the nervous system so that muscles with similar contributions to task variables are controlled together. Alternatively, this covariation might reflect regulation of low-level aspects of movements that are common across tasks, such as stresses within joints. We examined these issues by analyzing covariation patterns in quadriceps muscle activity during locomotion in rats. The three monoarticular quadriceps muscles (vastus medialis [VM], vastus lateralis [VL], and vastus intermedius [VI]) produce knee extension and so have identical contributions to task performance; the biarticular rectus femoris (RF) produces an additional hip flexion. Consistent with the proposal that muscle covariation is related to similarity of muscle actions on task variables, we found that the covariation between VM and VL was stronger than their covariations with RF. However, covariation between VM and VL was also stronger than their covariations with VI. Since all vastii have identical actions on task variables, this finding suggests that covariation between muscle activity is not solely driven by simplification of overt task performance. Instead, the preferentially strong covariation between VM and VL is consistent with the control of internal joint stresses: Since VM and VL produce opposing mediolateral forces on the patella, the high positive correlation between their activation minimizes the net mediolateral patellar force. These results provide important insights into the interpretation of muscle covariations and their role in movement control.

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“…The idea that each synergy solves a well-defined control problem [e.g., to lead the system to a specific equilibrium position (Nori and Frezza, 2005)], appears in several other studies (Chhabra and Jacobs, 2008; Todorov, 2009; Alessandro and Nori, 2012). Chhabra and Jacobs (2008) propose a method called Greedy additive regression (GAR).…”

Section: Synergies From the Perspective Of The Task-spacementioning

confidence: 99%

“…Alessandro and Nori (2012) define synergies as parameterized functions of time that serve as feedforward controllers. The identification procedure consists in finding the values of the parameters such that appropriate linear combinations of the resulting synergies drive the dynamical system over a set of desired trajectories (training set).…”

Section: Synergies From the Perspective Of The Task-spacementioning

confidence: 99%

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

Alessandro¹,

Delis²,

Nori³

et al. 2013

Front. Comput. Neurosci.

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115

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In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well.

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Identification of synergies by optimization of trajectory tracking tasks

Cited by 11 publications

References 12 publications

A computational analysis of motor synergies by dynamic response decomposition

A computational analysis of motor synergies by dynamic response decomposition

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

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