Modularity of Motor Output Evoked By Intraspinal Microstimulation in Cats

Lemay, Michel; Grill, Warren M.

doi:10.1152/jn.00235.2003

Cited by 110 publications

(97 citation statements)

References 36 publications

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“…Presently, one can only speculate on the role of particular neural structures in all these examples of linear behavior of strongly nonlinear systems. Modulation of spinal reflexes (Nichols and Houk 1976), operation with spinal primitives (Mussa-Ivaldi et al 1994;Lemay and Grill 2004) and particular brain mechanisms of predictive behavior (Kawato et al 2003) have been implicated in tuning elements of the system to simplify control.…”

Section: Linear Effects In the Control Of Prehensionmentioning

confidence: 99%

Maintaining rotational equilibrium during object manipulation: linear behavior of a highly non-linear system

2005

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We address issues of simultaneous control of the grasping force and the total moment of forces applied to a handheld object during its manipulation. Six young healthy male subjects grasped an instrumented handle and performed its cyclic motion in the vertical direction. The handle allowed for setting different clockwise (negative) or counterclockwise torques. Three movement frequencies: 1, 1.5 and 2 Hz, and five different torques: −1/3, −1/6, 0, 1/6 and 1/3 Nm, were used. The rotational equilibrium was maintained by two means: (1) Concerted changes of the moments produced by the normal and tangential forces, specifically anti-phase changes of the moments during the tasks with zero external torque and in-phase changes during the non-zero-torque tasks, and (2) Redistribution of the normal forces among individual fingers such that the agonist fingers-the fingers that resist external torque-increased the force in phase with the acceleration, while the forces of the antagonist fingers-those that assist the external torque-especially, the fingers with the large moment arms, the index and little fingers, stayed unchanged. The observed effects agree with the principle of superposition-according to which some complex actions, for example, prehension, can be decomposed into elemental actions controlled independently-and the mechanical advantage hypothesis according to which in moment production the fingers are activated in proportion to their moment arms with respect to the axis of rotation. We would like to emphasize the linearity of the observed relations, which was not prescribed by the task mechanics and seems to be produced by specific neural control mechanisms.

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Section: Linear Effects In the Control Of Prehensionmentioning

confidence: 99%

Maintaining rotational equilibrium during object manipulation: linear behavior of a highly non-linear system

2005

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“…The model predicts a synergy control signal distributed to many motoneuronal pools and transformed into musclespecific activation waveforms. Several studies on spinalized or decerebrated frogs and cats (Tresch et al, 1999;Kargo and Giszter, 2000;Saltiel et al, 2001;Hart and Giszter, 2004;Lemay and Grill, 2004) indicate that the vertebrate spinal cord is capable of organizing muscle synergies and of flexibly combining them to generate various motor behaviors. In mammals, and particularly in primates, the control of reaching movements also involves the cerebral cortex, the cerebellum, and the basal ganglia.…”

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confidence: 99%

Control of Fast-Reaching Movements by Muscle Synergy Combinations

et al. 2006

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How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.

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“…MDS is unsupervised by nature in that it does not presuppose dissimilarity structure and it aims to reveal the composition that dominate the representation of stimuli in a given ROI by data-driven processes. Hierarchical clustering analysis assists in identifying relatively homogeneous groups of stimuli, using an algorithm that starts with each stimulus in a separate cluster and combines clusters based on their closeness until only one is left [26]. For the experimental condition with a small number of stimuli, similarity-graph is often used to visualize the representation where each of the nodes and edges respectively indicates a stimulus and how these different stimuli are similar together.…”

Section: Representing Dissimilarity Structure Of Response Patterns Tomentioning

confidence: 99%

Similarity representation of pattern-information fMRI

Xue

Weng

et al. 2013

Chin. Sci. Bull.

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Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. Similarity/dissimilarity is the core concept of RSA, realized by the construction of a representational dissimilarity matrix, that addresses the closeness/distance for each pair of research elements (e.g., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation. This approach is also welcome for its sensitivity in detecting subtle differences of distributed experimental effects in the brain. Importantly, RSA is not only an experimental tool but a promising data-analytical framework that can integrate cross-modal imaging signals, explore brain-behavior link, and verify computational models according to measured neural activities. RSA substantiates its integrative power by relating similarity structure in one domain (e.g., stimulus features) to that in another domain (e.g., neural activities). This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform. Several recent advances are highlighted, such as the extraction of across-subjects regularity, cross-validation of brain reactivity in human beings and monkeys, the incorporation of computational models and behavioral profiles into RSA. Voxel receptor field modeling, another promising multivariate tool of pattern elucidation, is presented and compared. The application of RSA is expected to surge and extend in many fields of neuroscience, computation, psychology and medicine. We also discuss the limitations of RSA and some critical questions that need to be addressed in future research.representational similarity analysis, pattern information, condition-rich experiments, decode Citation:Xue S W, Weng X C, He S, et al. Similarity representation of pattern-information fMRI.

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Modularity of Motor Output Evoked By Intraspinal Microstimulation in Cats

Cited by 110 publications

References 36 publications

Maintaining rotational equilibrium during object manipulation: linear behavior of a highly non-linear system

Maintaining rotational equilibrium during object manipulation: linear behavior of a highly non-linear system

Control of Fast-Reaching Movements by Muscle Synergy Combinations

Similarity representation of pattern-information fMRI

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