Spinal circuits form building blocks for movement construction. In the frog, such building blocks have been described as isometric force fields. Microstimulation studies showed that individual force fields can be combined by vector summation. Summation and scaling of a few force-field types can, in theory, produce a large range of dynamic force-field structures associated with limb behaviors. We tested for the first time whether force-field summation underlies the construction of real limb behavior in the frog. We examined the organization of correction responses that circumvent path obstacles during hindlimb wiping trajectories. Correction responses were triggered on-line during wiping by cutaneous feedback signaling obstacle collision. The correction response activated a force field that summed with an ongoing sequence of force fields activated during wiping. Both impact force and time of impact within the wiping motor pattern scaled the evoked correction response amplitude. However, the duration of the correction response was constant and similar to the duration of other muscles activated in different phases of wiping. Thus, our results confirm that both force-field summation and scaling occur during real limb behavior, that force fields represent fixed-timing motor elements, and that these motor elements are combined in chains and in combination contingent on the interaction of feedback and central motor programs.

Skill learning may be based on integrating and adapting movement building blocks organized in the CNS. We examined at what level integration and adaptation occur during early skill learning, the level of individual muscles, muscle synergies or combinations of synergies through time, and whether these operations are expressed through the primary motor cortex (M1). Forelimb muscle and M1 cell activity were recorded over the first day of training on a reach-to-grasp task in rodents. Independent components analysis was used to assess how well muscle activation patterns could be described as time-varying combinations of synergies. In 3 of 11 animals, prereach M1 activity predicted the activation of different combinations of independent components (ICs) to perform the task. With training, animals increasingly adopted postures and prereach patterns of M1 activity that supported activation of the more successful combination. With training, animals also adjusted the activation magnitude (6 of 11 animals) and weights (11 of 11) of specific ICs that constituted the selected combination. Weights represent how IC activation patterns were distributed to forelimb muscles; this distribution pattern was adapted with training. M1 cells (37 of 100) had task-related firing rates that were significantly correlated with IC activation patterns. Changes in M1 firing rates were associated with corresponding changes in either the activation magnitude or weights of the correlated IC. Our data suggest that early skill learning is expressed through selection and tuning of M1 firing rates, which specify time-varying patterns of synergistic muscle contractions in the limb.

Complex actions may arise by combining simple motor primitives. Our studies support individual premotor drive pulses or bursts as execution primitives in spinal cord. Alternatively, the fundamental execution primitives at the segmental level could be time-varying synergies. To distinguish these hypotheses, we examined sensory feedback effects during targeted wiping organized in spinal cord. This behavior comprises three bursts. We tested (1) whether feedback altered the structure of individual premotor drive bursts or primitives, and (2) whether feedback differentially modulated different drive bursts or pulses in the three burst sequence. At least two of the three bursts would need to always be comodulated to support a time-varying synergy. We used selective muscle vibration to control spindle feedback from a single muscle (biceps/iliofibularis). The structures of premotor drive bursts were conserved. However, biceps vibration (1) scaled the amplitudes of two bursts coactivated during the initial phase of wiping independently of one another without altering their phase, and (2) independently phase regulated the third burst but preserved its amplitude. Thus, all three bursts were regulated separately. Durations were unaffected. The independent effects depended on (1) time of vibration during wiping, (2) frequency of vibration, and (3) limb configuration. Because each of the three bursts was independently modulated, these data strongly support execution using individual premotor bursts rather than time-varying synergies at the spinal level of motor organization. Our data show that both sensory feedback and central systems of the spinal cord act in concert to adjust the individual premotor bursts in support of the straight and unimodal wiping trajectory.

Spinal circuits may organize trajectories using pattern generators and synergies. In frogs, prior work supports fixed-duration pulses of fixed composition synergies, forming primitives. In wiping behaviors, spinal frogs adjust their motor activity according to the starting limb position and generate fairly straight and accurate isochronous trajectories across the workspace. To test whether a compact description using primitives modulated by proprioceptive feedback could reproduce such trajectory formation, we built a biomechanical model based on physiological data. We recorded from hindlimb muscle spindles to evaluate possible proprioceptive input. As movement was initiated, early skeletofusimotor activity enhanced many muscle spindles firing rates. Before movement began, a rapid estimate of the limb position from simple combinations of spindle rates was possible. Three primitives were used in the model with muscle compositions based on those observed in frogs. Our simulations showed that simple gain and phase shifts of primitives based on published feedback mechanisms could generate accurate isochronous trajectories and motor patterns that matched those observed. Although on-line feedback effects were omitted from the model after movement onset, our primitive-based model reproduced the wiping behavior across a range of starting positions. Without modifications from proprioceptive feedback, the model behaviors missed the target in a manner similar to that in deafferented frogs. These data show how early proprioception might be used to make a simple estimate initial limb state and to implicitly plan a movement using observed spinal motor primitives. Simulations showed that choice of synergy composition played a role in this simplicity. To generate froglike trajectories, a hip flexor synergy without sartorius required motor patterns with more proprioceptive knee flexor control than did patterns built with a more natural synergy including sartorius. Such synergy choices and control strategies may simplify the circuitry required for reflex trajectory construction and adaptation.