Frontiers in Physiology (Jun 2023)

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

  • Jasmine A. Berry,
  • Ali Marjaninejad,
  • Francisco J. Valero-Cuevas,
  • Francisco J. Valero-Cuevas,
  • Francisco J. Valero-Cuevas

DOI
https://doi.org/10.3389/fphys.2023.1183492
Journal volume & issue
Vol. 14

Abstract

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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.

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