Muscles: Non-linear Transformers of Motor Neuron Activity

Abstract

Predicting movement from neural activity requires quantitative understanding of muscle response to motor neuron input. Muscles are sufficiently complicated that fulfilling this goal requires computer simulation. We therefore first explain in considerable detail one approach to modeling muscle. We then provide multiple examples of how muscle intrinsic properties and muscle diversity make straightforward predictions of how muscles transform neural input into movement impossible, including the dependence of muscle velocity on sarcomere number, the inadequacy of mean data in muscle modeling, the effects of muscle low-pass filtering, spike-number vs. spike frequency coding for contraction amplitude, how the role of passive muscle force in movement generation varies as a function of limb size, how muscles produce forces greater than their 'maximum force', energy conserving mechanisms, muscles that brake rather than produce movement, and how muscles can generate restoring responses (preflexes) to perturbing input in the absence of sensory feedback.

A Synthetic Nervous System Controls a Simulated Cockroach

“…Each muscle activation curve has four parameters that must be chosen: the amplitude, steepness, x-offset, and y-offset (see Equation (3)). We used data from insect muscles to pick the steepness and x-offset of the muscle activation curve such that it looked like the biologically observed data [51][52][53] (for an example, see Supplementary Materials (Section: Methods Explained)). The y-offset is constrained such that the muscle produces 0 N of active tension when the motor-neuron is at rest.…”

Corn Snakes Show Consistent Sarcomere Length Ranges Across Muscle Groups and Ontogeny

“…In contrast, the plantaris muscle (an ankle extensor with an elastic tendon) shortens from the descending limb onto the plateau to match the increasing resistance while stretching the elastic tendon ( Azizi and Roberts 2010 ; Astley and Roberts 2012 ). Additionally, sarcomere length has been shown to aid in stability during locomotion via preflexes at timescales too fast for neural feedback due to the rising force on the ascending limb (along with contributions from the force-velocity relationship) ( Wagner and Blickhan 1999 ; Dickinson et al 2000 ; Hooper et al 2016 ).…”