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Published in:
Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Read chapter Read first chapter

2019 | OriginalPaper | Chapter

5. Evolving Spiking Neural Networks

Author : Nikola K. Kasabov

Published in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Publisher: Springer Berlin Heidelberg

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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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previous chapter Methods of Spiking Neural Networks
next chapter Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube
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Metadata
Title
Evolving Spiking Neural Networks
Author
Nikola K. Kasabov
Copyright Year
2019
Publisher
Springer Berlin Heidelberg
Book
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_5

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