From task parameters to motor synergies: A hierarchical framework for approximately optimal control of redundant manipulators

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...

supporting

confidence: 62%

Section: Review Of Hart and Gisztermentioning

confidence: 99%

On the Origins of Modularity in Motor Control

Delis

Chiovetto²,

Berret³

2010

“…Here, we show that the optimal feedback control theory that we (Todorov and Jordan, 2002a,b;Todorov, 2004Todorov, , 2005Todorov et al, 2005) and others (Meyer et al, 1988;Loeb et al, 1990;Hoff, 1992;Kuo, 1995;Scott, 2004) have pursued affords the flexibility apparent in behavioral data, in contrast with more traditional theories (Flash and Hogan, 1985;Uno et al, 1989;Bizzi et al, 1992;Feldman and Levin, 1995). We distinguish two forms of flexibility.…”

Section: Introductionmentioning

confidence: 64%

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Liú¹,

Todorov²

2007

390

400

Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.

“…These ideas may extend from motor control, into development, morphology, vocalization, and evolutionary psychology (Mowrey and MacKay, 1990;Samuels, 2000), but modularity is controversial (Giszter et al 2009, Tresch andJarc, 2009). Modularity could simply represent task structures and/or optimal control outcomes using a redundant limb (Sanger, 1994(Sanger, , 2000Todorov, 2004;Todorov et al, 2005;Chhabra and Jacobs, 2006). Conversely, modularity might represent strategies built into the CNS structure a priori.…”

Section: Introductionmentioning

confidence: 99%

A Neural Basis for Motor Primitives in the Spinal Cord

Hart

Giszter²

2010

244

194

Motor primitives and modularity may be important in biological movement control. However, their neural basis is not understood. To investigate this, we recorded 302 neurons, making multielectrode recordings in the spinal cord gray of spinalized frogs, at 400, 800, and 1200 m depth, at the L2/L3 segment border. Simultaneous muscle activity recordings were used with independent components analysis to infer premotor drive patterns. Neurons were divided into groups based on motor pattern modulation and sensory responses, depth recorded, and behavior. The 187 motor pattern modulated neurons recorded comprised 14 cutaneous neurons and 28 proprioceptive neurons at 400 m in the dorsal horn, 131 intermediate zone interneurons from ϳ800 m depth without sensory responses, and 14 motoneuron-like neurons at ϳ1200 m. We examined all such neurons during spinal behaviors. Mutual information measures showed that cutaneous neurons and intermediate zone neurons were related better to premotor drives than to individual muscle activity. In contrast, proprioceptive-related neurons and ventral horn neurons divided evenly. For 46 of the intermediate zone interneurons, we found significant postspike facilitation effects on muscle responses using spike-triggered averages representing short-latency postspike facilitations to multiple motor pools. Furthermore, these postspike facilitations matched significantly in both their patterns and strengths with the weighting parameters of individual primitives extracted statistically, although both were initially obtained without reference to one another. Our data show that sets of dedicated interneurons may organize individual spinal primitives. These may be a key to understanding motor development, motor learning, recovery after CNS injury, and evolution of motor behaviors.