用发育图元自动机生成能解决任务的类生命神经结构
Growing Reservoirs with Developmental Graph Cellular Automata
- 用发育图元自动机从单节点开始生长有向图
- 生成的神经网络在基准任务上表现优于传统模型
- 适合研究可塑性神经结构与自适应形态发生
发育图元自动机（DGCA）是一种新型形态发生模型，可从单节点种子生长有向图。本文表明，DGCA 可被训练用于生成神经网络储备池。储备池通过两类目标进行训练：任务驱动（使用 NARMA 系列任务）和任务无关（使用储备池指标）。结果表明，DGCA 能生长出多种专门化、类生命结构，有效解决基准任务，统计上优于相同任务下的典型储备池。这为开发具备可塑性的 DGCA 系统以及建模功能性和自适应形态发生奠定了基础。
原文摘要 · Abstract (English)
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.
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