Growing Reservoirs with Developmental Graph Cellular Automata

TL;DR

This paper introduces Developmental Graph Cellular Automata (DGCA), a new model for morphogenesis that can grow reservoirs capable of solving benchmark tasks and outperforming traditional reservoirs.

Contribution

It demonstrates that DGCAs can be trained to grow specialized, life-like reservoirs for task-driven and task-independent purposes, advancing morphogenesis modeling.

Findings

DGCAs can grow into diverse, life-like structures.

DGCAs outperform typical reservoirs on benchmark tasks.

Reservoirs can be trained for both task-driven and independent growth.

Abstract

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.