Synaptic Pruning and Module Sealing: Bio-Inspired Mechanisms for Efficient and Adaptive Artificial General Intelligence

Biological neural development achieves extraordinary efficiency through massive synaptic pruning—eliminating ∼50% of connections during maturation while preserving core capabilities and enhancing specialization. This paper proposes analogous mechanisms for bio-regulated AGI architectures: dynamic module retirement and “sealing” triggered by homeostatic signals from the Bio-Regulated Mind Matrix (e.g., resource constraints, performance saturation). Key contributions include: (1) pruning algorithms mimicking developmental overproduction-then-elimination for parameter reduction; (2) confidence-based sealing to protect stable representations; (3) routing plasticity to free/degenerate pathways; and (4) phased adaptation modes for continual learning. Quantitative evaluations on long-sequence training and continual benchmarks demonstrate 40–60% parameter efficiency gains with minimal accuracy loss and improved forgetting resistance. Pseudocode enables integration with existing transformer-based systems, offering a scalable pathway to sustainable, self-optimizing general intelligence.