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Chapter 1 Artificial Neurogenesis : An Introduction and Selective Review

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. T. Kowaliw (B) Institut des Systèmes Complexes Paris Île-de-France, CNRS, Paris, France e-mail: taras@kowaliw.ca N. Bredeche Sorbonne Universités, UPMC University Paris 06, UMR 7222 ISIR,F-75005 Paris, France e-mail: nicolas.bredeche@upmc.fr N. Bredeche CNRS, UMR 7222 ISIR,F-75005 Paris, France S. Chevallier Versailles Systems Engineering Laboratory (LISV), University of Versailles, Velizy, France e-mail: sylvain.chevallier@uvsq.fr R. Doursat School of Biomedical Engineering, Drexel University, Philadelphia, USA e-mail: rene.doursat@drexel.edu T. Kowaliw et al. (eds.), Growing Adaptive Machines, 1 Studies in Computational Intelligence 557, DOI: 10.1007/978-3-642-55337-0_1, © Springer-Verlag Berlin Heidelberg 2014