Artificial Neural Microcircuits for use in Neuromorphic System Design

Andrew Walter*, Shimeng Wu, Andy Tyrrell, Liam McDaid, Malachy McElholm, Nidhin Thandassery Sumithran, Jim Harkin, Martin Albrecht Trefzer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Artificial Neural Networks (ANNs) are one of the most widely employed forms of biomorphic computation. However (unlike the biological nervous systems they draw inspiration from) the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of complex training & learning tools that produce application specific ANNs, susceptible to pitfalls like overfitting. In this paper, an alternative approach is suggested, inspired by the role played in biology by Neural Microcircuits, the so called “fundamental processing elements” of organic nervous systems. How large neural networks can be assembled using Artificial Neural Microcircuits, intended as off-the-shelf components, is articulated; before showing the results of initial work to produce a catalogue of such Microcircuits though the use of Novelty Search. 

Original languageEnglish
Title of host publicationALife 2023: Ghost in the Machine
Subtitle of host publicationProceedings
PublisherMIT Press
Number of pages8
Publication statusPublished - 24 Jul 2023
EventALife 2023: Ghost in the Machine - University of Hokkaido , Sapporo, Japan
Duration: 24 Jul 202328 Jul 2023


ConferenceALife 2023

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.
  • ALife 2023

    Andrew Walter (Presenter)

    24 Jul 202328 Jul 2023

    Activity: Participating in or organising an eventConference participation

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