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.

人工神经网络 (ANNs) 是最广泛应用的生物启发计算形式之一。该论文探索了新的方法，通过借鉴生物学中神经微电路的作用，使用人工神经微电路 (ANMs) 来组装大型神经网络，以期成为现成的组件。论文展示了采用新颖性搜索方法生成此类微电路目录的初步结果，并讨论了扩展这项初步工作的努力，包括在这些努力中发现的挑战以及可能克服这些挑战的方法。