Growing Adaptive Machines

Volume 557 of the series Studies in Computational Intelligence pp 1-60


Artificial Neurogenesis: An Introduction and Selective Review

  • Taras KowaliwAffiliated withInstitut des Systèmes Complexes - Paris Île-de-France, CNRS Email author 
  • , Nicolas BredecheAffiliated withSorbonne Universités, UPMC University Paris 06CNRS
  • , Sylvain ChevallierAffiliated withVersailles Systems Engineering Laboratory (LISV), University of Versailles
  • , René DoursatAffiliated withSchool of Biomedical Engineering, Drexel University

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In this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development—the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this “meta-design” for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchangeability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature.