Modular Premotor Drives and Unit Bursts as Primitives for Frog Motor Behaviors

Abstract

Spinal cord modularity impacts on our understanding of reflexes, development, descending systems in normal motor control, and recovery from injury. We used independent component analysis and best-basis or matching pursuit wavepacket analysis to extract the composition and temporal structure of bursts in hindlimb muscles of frogs. These techniques make minimal a priori assumptions about drive and motor pattern structure. We compared premotor drive and burst structures in spinal frogs with less reduced frogs with a fuller repertoire of locomotory, kicking, and scratching behaviors. Six multimuscle drives explain most of the variance of motor patterns (ϳ80%). Each extracted drive was activated with pulses at a single time scale or common duration (ϳ275 msec) burst structure. The data show that complex behaviors in brainstem frogs arise as a result of focusing drives to smaller core groups of muscles. Brainstem drives were subsets of the muscle groups from spinal frogs. The 275 msec burst duration was preserved across all behaviors and was most precise in brainstem frogs. These data support a modular decomposition of frog behaviors into a small collection of unit burst generators and associated muscle drives in spinal cord. Our data also show that the modular organization of drives seen in isolated spinal cord is fine-tuned by descending controls to enable a fuller movement repertoire. The unit burst generators and their associated muscle synergies extracted here link the biomechanical "primitives," described earlier in the frog, rat, and cat, and to the elements of pattern generation examined in fictive preparations.

Central and Sensory Contributions to the Activation and Organization of Muscle Synergies during Natural Motor Behaviors

“…By systematic and quantitative analyses of EMGs from a large number of muscles, we have established the sufficiency of a small number of synergies to describe both the intact and deafferented data sets well. Thus, our results support the hypothesis that the CNS simplifies the daunting task of deriving appropriate muscle patterns for many behaviors by flexible combination of a small number of synergies (Tresch et al, 1999;Hart and Giszter, 2004;Ting and Macpherson, 2005). Our findings also validate our means of identifying the synergies: the NMF algorithm would have failed to uncover any shared synergies if there had been a gross mismatch between the assumptions of the algorithm (such as linearity) and our conceptual model of synergy combination.…”

“…Previous experiments have suggested that the CNS may simplify control by activating a small number of modules (Grillner, 1985;Bizzi et al, 1991;Stein and Smith, 1997;Tresch et al, 2002), formulated as muscle synergies, or activation profiles across a set of muscles, the linear combination of which can generate diverse motor patterns (Tresch et al, 2002;Hart and Giszter, 2004;Ting and Macpherson, 2005). Previous studies examining this hypothesis have relied on animals with intact afferents (Saltiel et al, 2001) or have characterized predominantly feedforward, ballistic movements, such as cutaneous reflexes (Tresch et al, 1999) and kicking (d'Avella et al, 2003).…”

The use of flexible arm muscle synergies to perform an isometric stabilization task

“…To ensure that similar PCs were compared irrespective of the amount of variance explained by those PCs, we first computed the dot product of a given PC in a subject with all the three PCs in other subjects. The largest dot product was then considered (see Tresch et al, 1999;Hart and Giszter, 2004). We found that on average the correlation between PCs was 0.77 (± 0.02 SE) and 0.66 (± 0.01 SE) for the Step-push and Step-pull conditions respectively, confirming their similarity across subjects.…”

Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics

“…According to one common proposal, the CNS might produce movement through the flexible combination of ''muscle synergies,'' with each such synergy specifying a particular balance of activation across a set of muscles (7)(8)(9)(10)(11)(12)(13)(14)(15)(16). By reducing the number of controlled variables, such a lowdimensional control strategy would simplify the production of movement.…”