We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021), of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned. In Lynch and Mallmann-Trenn (Neural Networks, 2021), we considered simple tree-structured concepts and feed-forward layered networks. Here we extend the model in two ways: we allow limited overlap between children of different concepts, and we allow networks to include feedback edges. For these more general cases, we describe and analyze algorithms for recognition and algorithms for learning.

翻译：我们延续Lynch和Mallmann-Trenn（《神经网络》期刊，2021年）的研究，探讨具有分层结构的概念如何在类脑神经网络中表征，这些表征如何用于识别概念，以及这些表征如何通过学习获得。在Lynch和Mallmann-Trenn（《神经网络》期刊，2021年）的研究中，我们考虑了简单的树状结构概念和前馈分层网络。本文从两个方面扩展该模型：允许不同概念的子节点之间存在有限重叠，并允许网络包含反馈连接。针对这些更一般的情况，我们描述并分析了识别算法与学习算法。