For what I'm reading now the system I've independently developed is closest in spirit to Reservoir Computing, Artificial Life, and Agent-Based Modeling, and to a certain extent, to the broader field of Decentralised AI. It shares their focus on emergent behaviour from local interactions, but it's differentiated by its lack of explicit learning mechanisms, optimisation goals, and symbolic reasoning, making it a distinct kind of complex system simulation rather than a direct mapping to any AI technique.
The core idea is using dynamical systems, with their own complex local rules and interactions, to observe the system-level behaviour. It's a simulation model designed to explore a particular set of dynamics rather than a direct implementation of a learning model. This makes it a valuable tool in complex systems research, even if it is independently developed and not aligned with the mainstream of modern AI techniques.
The animation shows it's stable after 30000 cycles, with no new energy injected into the system.