Proprioceptive signals, often referred to as the “sixth sense,” are sensory inputs from the body that provide information about joint position, muscle tension, movement, and spatial orientation. These signals allow the body to sense its position and movements without relying on visual cues.
Proprioception is mediated by proprioceptors, sensory receptors, located within muscles, tendons, and joints. Most animals possess multiple subtypes of proprioceptors, which detect distinct kinesthetic parameters, such as joint position, movement, and load. Although all mobile animals possess proprioceptors, the structure of the sensory organs can vary across species.
Proprioceptive signals are transmitted to the central nervous system, where they are integrated with information from other sensory systems, such as the visual system and the vestibular system, to create an overall representation of body position, movement, and acceleration. In many animals, sensory feedback from proprioceptors is essential for stabilizing body posture and coordinating body movement.
Sources of Proprioceptive Signals
Muscle Spindles:
Located in skeletal muscles.
Detect changes in muscle length and the speed of these changes.
Golgi Tendon Organs:
Found in tendons.
Monitor tension generated by muscle contraction.
Joint Receptors:
Located in joint capsules and ligaments.
Provide information about joint angle and movement.
Cutaneous Receptors:
Found in the skin.
Contribute to proprioception by sensing pressure and stretch.
Research
A new study led by Alexander Mathis at EPFL now sheds light on the question by exploring how our brains create a cohesive sense of body position and movement. Published in Cell, the study was carried out by PhD students Alessandro Marin Vargas, Axel Bisi, and Alberto Chiappa, with experimental data from Chris Versteeg and Lee Miller at Northwestern University
The researchers used this musculoskeletal modeling to generate muscle spindle signals in the upper limb to generate a collection of “large-scale, naturalistic movement repertoire”. They then used this repertoire to train thousands of “task-driven” neural network models on sixteen computational tasks, each of which reflects a scientific hypothesis about the computations carried out by the proprioceptive pathway, which includes parts of the brainstem and somatosensory cortex.
The approach allowed the team to comprehensively analyse how different neural network architectures and computational tasks influence the development of “brain-like” representations of proprioceptive information. They found that neural network models trained on tasks that predict limb position and velocity were most effective, suggesting that our brains prioritize integrating the distributed muscle spindle input to understand body movement and position.
Proprioception revisited: where do we stand? – PMC
Task-driven neural network models predict neural dynamics of proprioception: Cell