scholarly journals Learning of action through adaptive combination of motor primitives

Nature ◽  
2000 ◽  
Vol 407 (6805) ◽  
pp. 742-747 ◽  
Author(s):  
Kurt A. Thoroughman ◽  
Reza Shadmehr
Author(s):  
Wilian dos Santos ◽  
Samuel Lourenco ◽  
Adriano Siqueira ◽  
Polyana Ferreira Nunes

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.


2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
F. Bajramovic ◽  
B. Deutsch ◽  
Ch. Gräßl ◽  
J. Denzler

2018 ◽  
Vol 69 ◽  
pp. 113-124 ◽  
Author(s):  
Juan Carlos Perafan Villota ◽  
Felipe Leno da Silva ◽  
Ricardo de Souza Jacomini ◽  
Anna Helena Reali Costa

Author(s):  
Ilaria Mileti ◽  
Aurora Serra ◽  
Nerses Wolf ◽  
Victor Munoz-Martel ◽  
Antonis Ekizos ◽  
...  

AbstractThe use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground, and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.


Author(s):  
Alessandro Santuz ◽  
Antonis Ekizos ◽  
Yoko Kunimasa ◽  
Kota Kijima ◽  
Masaki Ishikawa ◽  
...  

AbstractWalking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.


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