Growing Adaptive Machines pp 1-60

Part of the Studies in Computational Intelligence book series (SCI, volume 557) | Cite as

Artificial Neurogenesis: An Introduction and Selective Review

  • Taras Kowaliw
  • Nicolas Bredeche
  • Sylvain Chevallier
  • René Doursat

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Taras Kowaliw
    • 1
  • Nicolas Bredeche
    • 2
    • 3
  • Sylvain Chevallier
    • 4
  • René Doursat
    • 5
  1. 1.Institut des Systèmes Complexes - Paris Île-de-FranceCNRSParisFrance
  2. 2.Sorbonne Universités, UPMC University Paris 06ParisFrance
  3. 3.CNRSParisFrance
  4. 4.Versailles Systems Engineering Laboratory (LISV)University of VersaillesVelizyFrance
  5. 5.School of Biomedical EngineeringDrexel UniversityPhiladelphiaUSA

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