In this paper we investigate a neural network model in which weights between computational nodes are modified according to a local learning rule. To determine whether local learning rules are sufficient for learning, we encode the network architectures and learning dynamics genetically and then apply selection pressure to evolve networks capable of learning the four boolean functions of one variable. The successful networks are analysed and we show how learning behaviour emerges as a distributed property of the entire network. Finally the utility of genetic algorithms as a tool of discovery is discussed.

翻译：本文研究一种神经网络模型，其中计算节点之间的权重根据局部学习规则进行调整。为探究局部学习规则是否足以实现学习，我们将网络架构与学习动态进行遗传编码，并施加选择压力以演化出能够学习单变量四种布尔函数的网络。我们对成功网络进行分析，展示了学习行为如何作为整个网络的分布式特性而涌现。最后，讨论了遗传算法作为发现工具的有效性。