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Artificial Neurogenesis: An Introduction and Selective Review

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

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.

Keywords

  • 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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Notes

  1. 1.

    Further complicating this picture are recent results showing that these connections might themselves be information processing units, which would increase this estimation by several orders of magnitude [196].

  2. 2.

    By epigenetic, we mean here any heritable and non-genetic changes in cellular expression. (The same term is also used in another context to refer strictly to DNA methylation and transcription-level mechanisms.) This includes processes such as learning for an animal, or growing toward a light source for a plant. The mentioned time scale represents a rough average over cellular responses to environmental stimuli.

  3. 3.

    The authors point out a similarity between their developed NNs and natural networks, specifically the existence of higher-order network triads. However, Milo et al. [202] attribute the existence of such triads in natural networks to the minimization of information processing time, a factor which was not relevant to the NNs. Hence, we consider this similarity unexplained.

  4. 4.

    Caveat: while neuroevolution is known to be more versatile than, for instance, classic CoNN algorithms, it is also known that evolutionary computation will be often hindered by local optima in the fitness landscape, suggesting a possibly different sort of suboptimality.

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Kowaliw, T., Bredeche, N., Chevallier, S., Doursat, R. (2014). Artificial Neurogenesis: An Introduction and Selective Review. In: Kowaliw, T., Bredeche, N., Doursat, R. (eds) Growing Adaptive Machines. Studies in Computational Intelligence, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55337-0_1

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