Edge Computing in Nature: Minimal pre-processing of multi-muscle ensembles of spindle signals improves discriminability of limb movements

Abstract

Multiple proprioceptive signals, like those from muscle spindles, are thought to enable robust estimates of body configuration. Yet, it remains unknown whether spindle signals suffice to discriminate limb movements. Here, a simulated 4-musculotendon, 2-joint planar limb model produced repeated cycles of five end-point trajectories in forward and reverse directions, which generated spindle Ia and II afferent signals (proprioceptors for velocity and length, respectively) from each musculotendon. We find that cross-correlation of the 8D time series of raw firing rates (four Ia, four II) cannot discriminate among most movement pairs (∼ 29% accuracy). However, projecting these signals onto their 1st and 2nd principal components greatly improves discriminability of movement pairs (82% accuracy). We conclude that high-dimensional ensembles of muscle proprioceptors can discriminate among limb movements—but only after dimensionality reduction. This may explain the pre-processing of some afferent signals before arriving at the somatosensory cortex, such as processing of cutaneous signals at the cat’s cuneate nucleus.

An alpha- to gamma-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement: A computational study

“…We used a computational model of a Rhesus Macaque arm with 25 muscles to test whether velocity-dependent stretch reflexes (i.e., simple positive feedback monosynaptic simulating Ia afferents) are sufficiently disruptive to require active or predictive modulation to produce accurate movements in realistic multi-articular limbs. Our results show that the disruptions of the movements caused by the velocity-dependent stretch reflexes are large, variable, and task-dependent enough to need inhibition, as has been proposed-but never quantified-by Sherrington and others [8,19,22,29,30]. We then demonstrate a generalizable spinal regulatory mechanism (similar to, but distinct from, α − γ co-activation ) that significantly reduces disruptions caused by unregulated velocity-dependent stretch reflexes .…”

“…It is in this context that Sherrington mentioned that 'Inhibition is as important as excitation' [4]: in single-joint movements driven by an agonist-antagonist muscle pair, reciprocal inhibition of the antagonist α-MNs provided by Ia inhibitory interneuron mitigate the disruption of voluntary movements [18,21]. However, this simplified conceptual framework for reciprocal inhibition is difficult to extend and generalize to limbs driven by numerous multi-articular muscles where the roles of agonist and antagonist become unclear and can change during the movement [19,20,22,23].…”

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

“…Nature, in contrast, has evolved hierarchical distributed sensorimotor neural architectures, where computation happens throughout (centrally, in middleware, and 'the edge'). This form of biological edge computing happens at subcortical, spinal, and even anatomical levels [97][98][99]. Therefore, successful smart neuro-assistive or neuro-rehabilitation devices (which are, in fact, a hybrid human+robot system engaged in a game-theoretic dance) would, like robots in general, do well to learn from such forms of biological edge computing for physical action.…”