On the Origins of Modularity in Motor Control

Abstract

The control of movement is highly complex because of the biomechanical redundancy of the musculoskeletal system (Bernstein, 1967). To cope with the large number of degrees of freedom, humans and animals likely rely on a modular control architecture. In other words, the CNS may activate flexible combinations of motor primitives instead of controlling each muscle independently, a motor primitive being a premotor drive generated by some neuronal population (for example, in the spinal cord) that recruits a covarying group of muscles that remain in a fixed relationship during recruitment (Hart and Giszter, 2010). Hence, motor primitives may represent the building blocks of movement organization. An important direction for research is to investigate the neural basis of this organization in the spinal cord, i.e., the neural mechanisms that select the muscle activation patterns required to achieve a behavioral goal.In a recent study published in The Journal of Neuroscience, Hart and Giszter (2010) addressed this question in an innovative way, by developing a new methodology linking the neural input to the motor output. They supported the hypothesis that modularity is directly embodied in the neural circuitry of the spinal cord. The authors performed simultaneous recordings of muscle and neural activities of anesthetized spinal bullfrogs. Using three types of stimulation (muscle palpation, light touch stimulation, and manipulation of limb position), each one involving a different sensorimotor reflex pathway, they recorded EMGs from the bullfrog right hindlimb and neural data from three sites (depths) of the spinal cord.Hart and Giszter (2010) extracted primitives from the EMG data using the linear statistical method of independent component analysis (ICA). Using the standard assumption that the components accounting for the most EMG variance represent estimates of motor primitives (Ivanenko et al., 2005), they proceeded to investigate how the recorded neural activity was related to the extracted components and to the original EMG recordings. If neuronal firing correlated significantly better with component-based than with EMG representations of motor activity, such a result would be a strong argument in favor of the hypothesis that specific neural activities associate with primitives. A novelty of this study was the use of information-theoretic measures, which quantify associations between random variables, including dependencies that cannot be captured by linear regression methods. Hart and Giszter used the mutual information coefficient (MI), measuring the amount of information that the firing rate shared with the independent components or EMG values. This method suggested that neuronal firing was more closely related to independent components than to contraction of individual muscles [Hart and Giszter (2010), their Fig. 4].Hart and Giszter (2010) also showed that the correlation between neuronal firing and motor components occurred for neurons in a specific region of the spinal cord [the intermediate zone (IZ)] (see...

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

“…Finally, approaches based on single-trial analysis of neural activity could also be instrumental in clarifying the existence of a neural basis for the muscle synergies (Hart and Giszter, 2004, 2010; Nazarpour et al, 2012; Ranganathan and Krishnan, 2012). For example, they could in principle be applied to decode the task from single-trial neural population patterns that regulate the activation of synergies, and also to determine which patterns encode task differences, and which carry additional or independent information to that carried by other patterns (Delis et al, 2010). …”

“…In addition, Hart and Giszter (2010) showed that some interneurons of the frog spinal cord were better correlated with temporal synergies than with individual muscles. Therefore, they suggested that these neural populations constitute a neural basis for synergistic muscle activations (Delis et al, 2010). Another study demonstrated that the sequential activation of populations of neurons in the cat's motor cortex initiates and sequentially modifies the activity of a small number of functionally distinct groups of synergistic muscles (Yakovenko et al, 2010).…”

A unifying model of concurrent spatial and temporal modularity in muscle activity

“…We believe that application of our new extraction algorithm to simultaneously recorded neural and muscle activities may allow identifying invariant temporal and spatial neural patterns and relating them explicitly to both primitives (i.e., temporal modules) and muscle synergies (i.e., spatial modules). Hence, modeling neural and EMG data with the space-by-time decomposition may help to uncover the direct link among the different levels of the hierarchical organization of motor control and lead to new insights in the quest of understanding the neural basis of modularity in the control of movements (Bizzi and Cheung 2013;Delis et al 2010).…”

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach

“…A prominent example of such modularity is given by muscle synergies (D'Avella et al, 2003; Ting and McKay, 2007), loosely defined as stereotyped patterns of coordinated activations of groups of muscles. According to this hypothesis, the muscle patterns driving movements originate from linear combinations of a small number of synergies presumably recruited by a premotor drive generated by some neuronal population (Delis et al, 2010; Hart and Giszter, 2010). …”