Artificial Neural Microcircuits as Building Blocks: Concept and
  Challenges
Andrew Walter Shimeng Wu Andy M. Tyrrell Liam McDaid Malachy McElholm Nidhin Thandassery Sumithran Jim Harkin Martin A. Trefzer
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.