Abstract
Evolving SNN (eSNN) are a class of SNN and also a class of ECOS (Chap. 2) where spiking neurons are created (evolved) and merged in an incremental way to capture clusters and patterns from incoming data. This gives a new quality of the SNN systems to become adaptive, fast trained and to capture meaningful patterns from the data, departing the “curse of the black box neural networks’ and the “curse of catastrophic forgetting” as manifested by some traditional ANN models (Chap. 2). The inspiration comes from the brain as the brain always evolves its structure and functionality through continuous learning. It is always evolving and forming new knowledge.
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Acknowledgements
Parts of the material in this chapter have been published previously as referenced in the corresponding sections. I acknowledge the contribution of my co-authors in these publications: L. Benuskova. S. Wysoski, S. Schliebs, S. Soltic, A. Mohemmed, K. Double, N. Nuntalid, G. Indiveri.
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Kasabov, N.K. (2019). Evolving Spiking Neural Networks. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_5
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