Forward and reverse engineering the pain system: from computational neuroscience to neuro-engineering

Area of Science:

• Computational neuroscience
• Pain research
• Systems neuroscience

Background:

• Pain is a complex phenomenon involving sensory, motivational, and cognitive aspects.
• Computational approaches are crucial for bridging theoretical frameworks with empirical data in pain research.

Purpose of the Study:

• To review computational approaches for understanding pain.
• To illustrate their application in pain prediction, avoidance, and modulation.
• To highlight their potential in clinical pain management.

Main Methods:

• Review of key computational frameworks: reinforcement learning, control theory, Bayesian inference, and active inference.
• Application of forward and reverse engineering techniques for model refinement and hypothesis generation.

Main Results:

• Computational models provide descriptive, mechanistic, and normative explanations of pain.
• These approaches facilitate understanding of pain prediction, avoidance, and modulation.
• Synergistic use of engineering techniques refines models and generates testable hypotheses.

Conclusions:

• Computational frameworks advance fundamental neuroscience of pain.
• Potential for computational phenotyping and personalized therapies in pain management.
• Informs adaptive neuro-engineering interventions for pain.