Infrastructure That Learns: Network Neuroplasticity and Graph-Theoretic Growth in Autonomous Cellular Adaptive Infrastructure

This paper presents the long-term adaptive learning capacity of the Adaptive Matrix Ecosystem (AME), modelled on biological neuroplasticity and Hebbian learning principles. AMW maintenance missions permanently deposit IPS passages as they operate, causing the network to strengthen frequently-used pathways and accumulate redundant routing over operational time without any explicit learning algorithm or centralised design. Four convergence theorems establish formal guarantees: monotone graph growth, speed-of-healing convergence, monotone reachability growth, and expected redundancy non-decrease. Biological analogies — Hebbian synaptic potentiation, Physarum slime mold, fungal mycelium, trabecular bone…