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The influence of computational traits on the natural selection of the nervous system

Natural Computing volume 17, pages 403–425 (2018)Cite this article

Abstract

This article addresses why the neural network model has been selected by nature against other computational models to generate behavior in complex multicellular clades. This question, which has not yet been addressed in research, should not be ignored because understanding this issue is necessary to have a complete picture of the evolutionary process of the nervous system. The starting point to discuss the issue is a proposal made 30 years ago: the free-moving hypothesis. This proposal establishes prediction as the main function of the brain and that all multicellular organisms that move require a brain in order to make predictions. This article contains a review contrasting this hypothesis with the discoveries made in the last 30 years within different biological kingdoms. Although none of these discoveries contradict the free-moving hypothesis, it still does not answer the main question. Alternative hypotheses about the origin of the nervous system are discussed in this paper, but they also are not able to answer the question. Six hypotheses are proposed as possible answers, and each of them is discussed by comparing neural processing systems with three other alternative processing systems. The result is that the neural processing system is selected against other kinds of processing systems because it has computational robustness to damage, allowing its function of prediction to be more durable. While this result, called the first neural processing principle, answers the initial question and permits a finished proof of the free-moving hypothesis, it gives rise to the question of how computationally robust a system must be to be selected by nature. This paper claims that the system selected must be computationally robust enough to have enough offspring to allow variation. This answer, named the second neural principle, determines the minimum amount of neurons that a neural processing system must have, but not the maximum. To address this issue, the third and fourth neural processing principles are stated, which determine that the maximum number of neurons is limited by energetic restrictions and body size, respectively. The results presented in this paper show that computational robustness is an important parameter to understand the evolution of nervous system.

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Fig. 1

Notes

  1. Astrocytes also play an important role in the functions of neurons.

  2. Neural network models that overcome the Church-Turing limit are not considered because there is no experimental evidence that this happens in nature (Siegelmann 1998).

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Acknowledgements

Sergio Miguel-Tomé would like to thank Dr. Rodolfo Llinás for conversations that served as the origin of this paper and for encouraging me to write this article. He would also like to acknowledge to Dr. Ángel Porteros, Dr. Luis Alonso Romero and Lori-Ann Tuscan who proofread the paper and gave me suggestions for improvement. Finally, he would like to thank anonymous referees for their comments on this paper

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    Sergio Miguel-Tomé

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Miguel-Tomé, S. The influence of computational traits on the natural selection of the nervous system. Nat Comput 17, 403–425 (2018). https://doi.org/10.1007/s11047-017-9619-0

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