Artificial Neural Networks (ANNs) are one of the most widely employed forms of bio-inspired computation. However the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of complex training and learning tools that produce application specific ANNs, susceptible to pitfalls such as overfitting. In this paper, an new approach is explored, inspired by the role played in biology by Neural Microcircuits, the so called ``fundamental processing elements'' of organic nervous systems. How large neural networks, particularly Spiking Neural Networks (SNNs) can be assembled using Artificial Neural Microcircuits (ANMs), intended as off-the-shelf components, is articulated; the results of initial work to produce a catalogue of such Microcircuits though the use of Novelty Search is shown; followed by efforts to expand upon this initial work, including a discussion of challenges uncovered during these efforts and explorations of methods by which they might be overcome.

翻译：人工神经网络（ANNs）是应用最广泛的仿生计算方法之一。然而当前趋势显示，人工神经网络在结构上趋于同质化。这种结构同质性要求应用复杂的训练与学习工具来生成特定应用场景的网络，易陷入过拟合等陷阱。本文探索了一种受神经微回路——有机神经系统中所谓的"基本处理单元"——在生物学中所发挥作用的启发的新方法。阐述了如何利用作为现成组件的人工神经微回路（ANMs）组装大规模神经网络（特别是脉冲神经网络SNNs）；展示了通过新颖性搜索（Novelty Search）初步建立此类微回路目录的研究成果；进而介绍了在初步工作基础上的拓展研究，包括对这些过程中发现挑战的讨论以及可能的解决方法的探索。