Advancing spine connectomics and neural integration through machine learning and neuroengineering: a narrative review

Area of Science:

• Neuroscience
• Spinal Cord Systems Neuroscience

Background:

• Spinal connectomics is redefining the spinal cord's role from a reflex relay to an active sensorimotor integrator.
• Understanding spinal circuitry is key to explaining adaptive motor control and flexible movement.

Purpose of the Study:

• To review recent advances in spinal circuitry research.
• To examine how spinal networks contribute to flexible, context-dependent motor behavior.

Main Methods:

• Review of experimental and computational studies.
• Focus on high-density electrophysiology, advanced imaging, circuit mapping, and computational modeling.

Main Results:

• Spinal circuits may employ principles like predictive coding and Bayesian integration.
• High-density techniques enable laminar-specific analysis; models link circuit organization to function.
• Advances inform neuromodulation, rehabilitation, and brain-machine interfaces for spinal cord injury.

Conclusions:

• The spinal cord is increasingly viewed as a dynamic computational system.
• Integrating connectomics, modeling, and neuromodulation offers a framework for spinal function and therapy.
• Progress in neural interfaces and modeling will advance spinal neuroscience and neurological disorder treatment.