Overview
- Covers recent advances and applications of spiking neural-like computational models.
- Includes unprecedented information on the use of spiking neural-like computing models to solve real-world problems
- Presents spiking neural methods that are suitable for different applications, such as data mining, CV, and NLP
Part of the book series: Computational Intelligence Methods and Applications (CIMA)
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About this book
Membrane computing is a class of distributed and parallel computing models inspired by living cells. Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks. Models and applications constitute two major research topics in spiking neural P systems. The entire book comprises two parts: models and applications.
In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems.
In the application part, the application of spiking neural P systems in time series prediction, image processing, sentiment analysis, and fault diagnosis is examined. This offers a novel method and model for researchers in artificial intelligence, data mining, image processing, natural language processing, and power systems. Simultaneously, it furnishes engineering and technical personnel in these fields with a powerful, efficient, reliable, and user-friendly set of tools and methods.
Keywords
Table of contents (8 chapters)
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Models
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Applications
Authors and Affiliations
About the authors
Hong Peng is the Professor at School of Computer and Software Engineering at Xihua University, Chengdu, China. The member of the “International Membrane Computing Society (IMCS)”, IEEE member and CCF member. He is the main investigator of 4 scientific research projects funded by National Natural Science Foundation of China and of more than 20 scientific research projects at the national and provincial levels. He was awarded Sichuan Provincial Natural Science Award in 2017. His research interests include membrane computing, artificial neural works, deep learning, image and computer vision, natural language processing. He has published over 170 scientific papers in international journals and conferences. He has more than 3200 citations with an H-index of 33, according to Google Scholar.
Jun Wang is the Professor at School of Electrical Engineering and Electronic Information at Xihua University, Chengdu, China. The member of the “International Membrane Computing Society (IMCS)”. She is the main investigator of 3 scientific research projects funded by National Natural Science Foundation of China and of more than 30 scientific research projects at the national and provincial levels. She was awarded Sichuan Provincial Natural Science Award in 2017. Her research interests cover several topics, including membrane computing, artificial intelligence, machine learning, intelligent control, power systems. She has published over 90 scientific papers in international journals and conferences. He has more than 2300 citations with an H-index of 29, according to Google Scholar.
Bibliographic Information
Book Title: Advanced Spiking Neural P Systems
Book Subtitle: Models and Applications
Authors: Hong Peng, Jun Wang
Series Title: Computational Intelligence Methods and Applications
DOI: https://doi.org/10.1007/978-981-97-5280-5
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-97-5279-9Published: 23 August 2024
Softcover ISBN: 978-981-97-5282-9Due: 06 September 2025
eBook ISBN: 978-981-97-5280-5Published: 22 August 2024
Series ISSN: 2510-1765
Series E-ISSN: 2510-1773
Edition Number: 1
Number of Pages: XIV, 297
Number of Illustrations: 29 b/w illustrations, 107 illustrations in colour
Topics: Artificial Intelligence, Theory of Computation, Computer Imaging, Vision, Pattern Recognition and Graphics, Signal, Image and Speech Processing, Natural Language Processing (NLP), Machine Learning