Abstract
Biological neural networks (BNNs) have inspired the creation of artificial neural networks (ANNs) [19]. One of the properties of BNNs is computational robustness, but this property is often overlooked in computer science because ANNs are usually virtualizations executed in a physical machine that lacks computational robustness. However, it was recently proposed that computational robustness could be a key feature that drives the selection of the computational model in the evolution of animals [20]. Until now, only energetic cost and processing time had been considered as the features that drove the evolution of the nervous system. The new standpoint leads us to consider whether computational robustness could have driven the evolution of not only the computational model but also other nervous system traits in animals through the process of natural selection. Because an important feature of an animal’s nervous system is its neural code, we tested the relationship among the computational properties of feed-forward neural networks and the neural codes through in silico experiments. We found two main results: There is a relationship between the number of epochs needed to train a feed-forward neural network using back-propagation and the neural code of the neural network, and a relationship exists between the computational robustness and the neural code of a feed-forward neural network. The first result is important to ANNs and the second to BNNs.
This research was supported by Universidad Isabel I. I would like to thank Lori-Ann Tuscan for proofreading this paper.
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Miguel-Tomé, S. (2019). An Experimental Study on the Relationships Among Neural Codes and the Computational Properties of Neural Networks. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_5
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