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 beginswith 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.
CITATION STYLE
Kowaliw, T., Bredeche, N., Chevallier, S., & Doursat, R. (2014). Artificial neurogenesis: An introduction and selective review. Studies in Computational Intelligence, 557, 1–60. https://doi.org/10.1007/978-3-642-55337-0_1
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