Learning to Use Muscles

Abstract

The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

Heksor: the central nervous system substrate of an adaptive behaviour

“…Results consistent with the heksor hypothesis would support the recent proposal that muscle synergies are not fixed patterns, but are instead habits that can adjust to changing sensorimotor demands (Loeb, 2021) (e.g. Sawers et al, 2015).…”

Neural Control of Stopping and Stabilizing the Arm

“…While this type of control may provide the ideal solution to an engineered system, this is not necessarily how the human CNS behaves, probably due to constraints imposed by evolution. Therefore, it is likely that we define a range of task-specific costs with different gains dependent on task conditions, and which allows us to rely on a local minimum that is “good enough,” i.e., satisficing rather than looking for the “optimal” solution ( Rosenbaum et al, 2001 ; De Rugy et al, 2012 ; Loeb, 2021 ). It is important to note here that neural control of movement is not “ideal” to begin with (i.e., our movements are not always the most energetically efficient or least erroneous choice), and any assumption of its ideal nature forms an incorrect basis for models of human movement.…”

A model for self-organization of sensorimotor function: spinal interneuronal integration

“…The various interneurons whose inputs and outputs have been characterized electrophysiologically have been organized into types based on the nature of the first discovered or most prominent aspects of their connectivity ( 5 – 7 ), but their complete patterns of connectivity are quite complex and inconsistent with conventional servocontrol ( 8 , 9 ). The aggregate set of interneurons has been modeled as a programmable multi-input-multi-output regulator whose design might be understood as the embodiment of a form of optimal control called linear quadratic regulator design ( 10 , 11 ), but it seems unlikely that the brain could compute the myriad weights required to achieve optimal control via such circuitry ( 12 , 13 ).…”