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，2021) 的研究，探索具有分层结构的概念如何在类似大脑的神经网络中表示、如何使用这些表示来识别概念以及如何学习这些表示。在 Lynch 和 Mallmann-Trenn (神经网络，2021) 中，我们考虑了简单的树形结构概念和前馈分层网络。 在这里，我们扩展了模型：允许不同概念子节点之间有限重叠，允许网络包含反馈边缘。对于这些更一般的情况，我们描述和分析了识别和学习算法。
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