Task-driven hierarchical deep neural network models of the proprioceptive pathway

Abstract

Biological motor control is versatile and efficient. Muscles are flexible and undergo continuous changes requiring distributed adaptive control mechanisms. How proprioception solves this problem in the brain is unknown. Here we pursue a task-driven modeling approach that has provided important insights into other sensory systems. However, unlike for vision and audition where large annotated datasets of raw images or sound are readily available, data of relevant proprioceptive stimuli are not. We generated a large-scale dataset of human arm trajectories as the hand is tracing the alphabet in 3D space, then using a musculoskeletal model derived the spindle firing rates during these movements. We propose an action recognition task that allows training of hierarchical models to classify the character identity from the spindle firing patterns. Artificial neural networks could robustly solve this task, and the networks’ units show directional movement tuning akin to neurons in the primate somatosensory cortex. The same architectures with random weights also show similar kinematic feature tuning but do not reproduce the diversity of preferred directional tuning nor do they have invariant tuning across 3D space. Taken together our model is the first to link tuning properties in the proprioceptive system to the behavioral level.HighlightsWe provide a normative approach to derive neural tuning of proprioceptive features from behaviorally-defined objectives.We propose a method for creating a scalable muscle spindles dataset based on kinematic data and define an action recognition task as a benchmark.Hierarchical neural networks solve the recognition task from muscle spindle inputs.Individual neural network units in middle layers resemble neurons in primate somatosensory cortex & make predictions for neurons along the proprioceptive pathway.