Artificial Neurogenesis: An Introduction and Selective Review

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


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.


Neural Network Extreme Learning Machine Deep Learning Neural Model Hebbian Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  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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