Growing Reservoirs with Developmental Graph Cellular Automata

2025-08-11

AI Summary

Conventional reservoir computing relies on manually designed network architectures and lacks self-organizing, evolvable structural dynamics. Method: This work proposes, for the first time, a developmental graph cellular automaton (DGCA)-based reservoir generation framework. Starting from a single-node seed, DGCA drives a life-like morphogenetic process to co-optimize task-driven objectives (e.g., NARMA benchmarks) and task-agnostic structural properties—including spectral radius, dynamic range, and memory capacity. Contribution/Results: The resulting reservoirs exhibit both functional specialization and structural plasticity, significantly outperforming baseline reservoirs (e.g., random, Erdős–Rényi) on NARMA-10 and related tasks. This study establishes a novel paradigm that imports principles of biological morphogenesis into reservoir computing, paving the way for adaptive, evolvable, and biologically inspired intelligent computing systems.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Train DGCAs to grow task-driven and task-independent reservoirs
• Develop DGCA systems for producing plastic, adaptive reservoirs
• Enable DGCAs to outperform typical reservoirs on benchmark tasks

Innovation

• Developmental Graph Cellular Automata model
• Task-driven and task-independent reservoir growth
• Life-like structures outperforming typical reservoirs