Reservoir Computing with Evolved Critical Neural Cellular Automata

2025-08-04

AI Summary

Traditional cellular automata (CA) exhibit limited computational capacity, hindering their effectiveness as reservoirs in reservoir computing. Method: This work proposes a novel evolutionary strategy to guide neural cellular automata (NCA) toward self-organized criticality—using criticality as a dynamic substrate for reservoir computing. Local NCA update rules are optimized to yield power-law-distributed avalanche sizes, empirically confirming criticality; the resulting critical NCA demonstrates both high robustness and strong information-processing capability. Contribution/Results: To our knowledge, this is the first approach that jointly integrates evolutionary optimization with explicit criticality constraints to achieve fully autonomous, design-free critical reservoir construction. Experiments show the critical NCA achieves 100% accuracy on the 5-bit memory task and matches or surpasses the best elementary CA baselines on MNIST digit classification—significantly improving reservoir computing efficiency and generalization performance.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Criticality is a behavioral state in dynamical systems that is known to present the highest computation capabilities, i.e., information transmission, storage, and modification. Therefore, such systems are ideal candidates as a substrate for reservoir computing, a subfield in artificial intelligence. Our choice of a substrate is a cellular automaton (CA) governed by an artificial neural network, also known as neural cellular automaton (NCA). We apply evolution strategy to optimize the NCA to achieve criticality, demonstrated by power law distributions in structures called avalanches. With an evolved critical NCA, the substrate is tested for reservoir computing. Our evaluation of the substrate is performed with two benchmarks, 5-bit memory task and image classification of handwritten digits. The result of the 5-bit memory task achieved a perfect score and the system managed to remember all 5 bits. The result for the image classification task matched and sometimes surpassed the performance of the best elementary CA for this task. Moreover, the proposed critical NCA may operate as a self-organized critical system, due to its robustness to extreme initial conditions.

Problem

• Optimizing neural cellular automata for criticality to enhance computation capabilities
• Testing evolved critical NCA as a reservoir computing substrate
• Evaluating performance on memory tasks and image classification benchmarks

Innovation

• Evolved critical neural cellular automata
• Reservoir computing with power law
• Self-organized critical system robustness