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. Author manuscript; available in PMC: 2017 Aug 8.
Published in final edited form as: Neural Comput. 2014 Mar 31;26(6):1128–1168. doi: 10.1162/NECO_a_00576

Figure 5.

Figure 5

Muscle synergies extracted by the JG algorithm were physiologically interpretable. A, Intact (black) and deafferented (white) muscle synergies for swimming (frog 2) returned by the Gaussian algorithm. The scalar product similarity between each synergy pair is indicated above the pair. The intact and deafferented synergies for pair 4 were totally dissimilar. B, Intact (black) and deafferented (white) muscle synergies for swimming (frog 2) returned by the JG algorithm. Here, even in the least similar pair (pair 4, scalar product = 0.62), the sets of muscles found to be active in the intact and deafferented synergies were still identical. C, The correlation coefficient between the activation of muscle synergy 3 (the extension synergy) and those of muscle synergies 1, 2, and 4, respectively, before (black) and after (white) deafferentation. Note that the correlation between synergies 3 and 4 increased 5-fold after deafferentation. This suggests that sensory feedback is essential in triggering or maintaining the activation of synergy 4 during the flexion phase of the swim cycle.