Modeling the potentiality of spinal-like circuitry for stabilization of a planar arm system

Tsianos, George A.; Raphael, Giby; Loeb, Gerald E.

doi:10.1016/b978-0-444-53815-4.00006-6

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Recent modeling work that includes the spinal cord circuitry provides interesting insight into this question. Indeed, a system with a large number of control inputs to a realistic set of interneuronal pathways was found to enable a simple learning algorithm to rapidly converge to physiological solutions (Raphael et al, 2010; Tsianos et al, 2011). Instead of reducing the dimensionality of control signals to assist computation of an unlikely global optimum, the nervous system might take advantage of the high probability of finding good-enough local minima within the high dimensional space of low-level circuitry (Loeb, 2012).…”

Section: Discussionmentioning

confidence: 99%

Are muscle synergies useful for neural control?

Rugy¹,

Loeb²,

Carroll³

2013

Front. Comput. Neurosci.

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131

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The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed 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. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.

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Section: Discussionmentioning

confidence: 99%

Are muscle synergies useful for neural control?

Rugy¹,

Loeb²,

Carroll³

2013

Front. Comput. Neurosci.

Self Cite

131

View full text Add to dashboard Cite

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“…Gradient-based learning using trajectory errors has in fact been demonstrated in a similar model of movement control using spinal-like neural networks (Raphael et al, 2010; Tsianos et al, 2011). On the other hand, performance starts decreasing immediately.…”

Section: Resultsmentioning

confidence: 97%

Spinal circuits can accommodate interaction torques during multijoint limb movements

Bührmann

Paolo

2014

Front. Comput. Neurosci.

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The dynamic interaction of limb segments during movements that involve multiple joints creates torques in one joint due to motion about another. Evidence shows that such interaction torques are taken into account during the planning or control of movement in humans. Two alternative hypotheses could explain the compensation of these dynamic torques. One involves the use of internal models to centrally compute predicted interaction torques and their explicit compensation through anticipatory adjustment of descending motor commands. The alternative, based on the equilibrium-point hypothesis, claims that descending signals can be simple and related to the desired movement kinematics only, while spinal feedback mechanisms are responsible for the appropriate creation and coordination of dynamic muscle forces. Partial supporting evidence exists in each case. However, until now no model has explicitly shown, in the case of the second hypothesis, whether peripheral feedback is really sufficient on its own for coordinating the motion of several joints while at the same time accommodating intersegmental interaction torques. Here we propose a minimal computational model to examine this question. Using a biomechanics simulation of a two-joint arm controlled by spinal neural circuitry, we show for the first time that it is indeed possible for the neuromusculoskeletal system to transform simple descending control signals into muscle activation patterns that accommodate interaction forces depending on their direction and magnitude. This is achieved without the aid of any central predictive signal. Even though the model makes various simplifications and abstractions compared to the complexities involved in the control of human arm movements, the finding lends plausibility to the hypothesis that some multijoint movements can in principle be controlled even in the absence of internal models of intersegmental dynamics or learned compensatory motor signals.

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“…In this experiment we would only like to demonstrate how our developmental model can exploit the learned reflex circuits to perform point-to-point trajectories in a way consistent with the TCT [15] (see also [59], [60] for a related model). The experiment is not intended to be a systematic analysis of the performance of the model on this type of tasks, as this will be a matter of future work, but simply to allow the reader to have a broader interpretation of our developmental model.…”

Section: Resultsmentioning

confidence: 99%

From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model

2014

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In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.

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Modeling the potentiality of spinal-like circuitry for stabilization of a planar arm system

Cited by 17 publications

References 17 publications

Are muscle synergies useful for neural control?

Are muscle synergies useful for neural control?

Spinal circuits can accommodate interaction torques during multijoint limb movements

From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model

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