Growing Reservoirs with Developmental Graph Cellular Automata
This work addresses the challenge of designing effective reservoir computing systems for machine learning applications, representing an incremental advancement in morphogenesis-based approaches.
The paper tackles the problem of growing reservoir computing structures using Developmental Graph Cellular Automata (DGCA), showing that DGCAs can grow directed graphs from single-node seeds to create specialized reservoirs that statistically outperform typical reservoirs on NARMA benchmark tasks.
Developmental Graph Cellular Automata (DGCA) are a novel model for morphogenesis, capable of growing directed graphs from single-node seeds. In this paper, we show that DGCAs can be trained to grow reservoirs. Reservoirs are grown with two types of targets: task-driven (using the NARMA family of tasks) and task-independent (using reservoir metrics). Results show that DGCAs are able to grow into a variety of specialized, life-like structures capable of effectively solving benchmark tasks, statistically outperforming `typical' reservoirs on the same task. Overall, these lay the foundation for the development of DGCA systems that produce plastic reservoirs and for modeling functional, adaptive morphogenesis.