Spiking Neural P Systems

Spiking neural P systems represent a significant advancement in the field of membrane computing, drawing inspiration from the communication patterns observed in neurons. Since their inception in 2006, these distributed and parallel neural-like computing models have gained popularity and emerged as important tools within the membrane computing area. As a key branch of the third generation of artificial neural networks, a fascinating research area of artificial intelligence, spiking neural P systems offer a captivating blend of theoretical elegance and practical utility. Their efficiency, Turing completeness, and real-life application characteristics, including interpretability and suitability for large-scale problems, have positioned them at the forefront of contemporary research in membrane computing and artificial intelligence.

This state-of-the-art reference work is organized into three parts comprising twelve chapters. It thoroughly investigates the theoretical foundations, real-life applications, and implementations of spiking neural P systems. From fundamental principles to computational power and complexity, the theoretical aspects are explored, laying the groundwork for understanding their practical applications. Real-life applications span a diverse range of domains, including complex optimization, classification, fault diagnosis, medical image processing, information fusion, cryptography, and robot control. Additionally, the book discusses several software and hardware implementations that provide valuable insights into the practical deployment of spiking neural P systems.

As the rapid development of spiking neural P systems continues to unfold, there is an increasing demand for a systematic and comprehensive summary of their capabilities and applications. This work serves as an invaluable resource for researchers, scholars, and practitioners interested in the theoretical underpinnings, algorithms, and practical implementation of artificial intelligence and membrane computing.

Discover the cutting-edge of spiking neural P systems, a captivating area of artificial neural network Provide comprehensive insights into foundations, applications, and implementations of the system Explore the most dynamic models in membrane computing and delves into the reasons behind

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Gexiang Zhang: Full Professor and the Dean of School of Automation at Chengdu University of Information Technology, Chengdu, China, also the Director of Research Institute of Autonomous Intelligence Technology and Systems (RIAITS). He is a foreign member of Russian Academy of Natural Sciences, President of International Membrane Computing Society (IMCS), IET Fellow and IEEE Senior Member. He is listed in World's Top 2% Scientists by Stanford University and in Highly Cited Chinese Researchers by Elsevier in several consecutive years. Research areas include membrane computing, artificial intelligence, robotics, power systems, and their interactions.

Sergey Verlan: Full Professor at the IUT Sénart-Fontainebleau at the University of Paris Est Créteil, France, member of the steering committees for the International Conference on Membrane Computing and for the conference Machines, Computations and Universality. Member of the International Membrane Computing Society (IMCS). His research interests cover several topics, including membrane computing, natural/DNA computing, unconventional computing, formal language theory, study of complex dynamic systems, and FPGA digital circuit design and applications in biological modelling and robotics.

Tingfang Wu: Associate Professor at the Department of Computer Science and Technology at Soochow University, Suzhou, China. He received his Ph.D. from the Huazhong University of Science and Technology in 2018, and focused on the theory of spiking neural P systems. His research interests cover several topics, including membrane computing, neural network, and bioinformatics.

Francis George C. Cabarle: Tenured associate professor at the Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines. His M.Sc. (2012) and Ph.D. (2015) degrees from the same university focused on theory and implementation of spiking neural P systems (SN P systems). He is a member of the International Membrane Computing Society (IMCS), part of the organising committees for the International Conferences on Membrane Computing (European and Asian editions), an editorial board member of the journal Discover Computing (Springer), and other international conferences.

Jie Xue: Full Professor, Young Talents of Dongyue Scholars at Shandong Normal University, member of the International Membrane Computing Society (IMCS). She mainly engaged in membrane computing, artificial intelligence algorithm and application in medical image management, especially for clinical problems of brain tumor, fundus lesion, and pancreatic cancer. She has published more than 40 journal papers in related fields.

David Orellana Martín: Assistant Professor at the Department of Computer Science and Artificial Intelligence at Universidad de Sevilla, he received his B.Sc. degree in 2014, his M.Sc. degree in 2016, and his Ph.D. degree in 2019 at the University of Seville. His main research interests are computational complexity theory, unconventional computing (especially membrane computing), as well as machine learning and high-performance computing.

Jianping Dong: Lecturer at the School of Automation at Chengdu University of Information Technology, Chengdu, China, He received his Ph.D. degree in 2024 from Chengdu University of Technology, Chengdu, China, and his M.S. degree at School of Electrical Engineering from Southwest Jiaotong University, Chengdu, China. His current research interests include membrane computing, image processing and optimization algorithms.

Luis Valencia-Cabrera: Associate Professor at the Department of Computer Science and Artificial Intelligence at Universidad de Sevilla. He received his degree in Computer Engineering, his MSc and his Ph.D. in Logic, Computation and Artificial In

Inhalt

Part I. Theoretical Aspects of Spiking Neural P Systems.- Chapter 1. Fundamentals of Spiking Neural P Systems.- Chapter 2. Computational Power of Spiking Neural P Systems.- Chapter 3. Computational Complexity of Spiking Neural P Systems.- Chapter 4. Variants of Spiking Neural P Systems.- Chapter 5. Automatic Design of Spiking Neural P Systems.- Part II. Real-world Applications of Spiking Neural P Systems.- Chapter 6. Complex Optimization with Spiking Neural P Systems.- Chapter 7. Classification with Spiking Neural P Systems.- Chapter 8. Fault Diagnosis with Spiking Neural P Systems.- Chapter 9. Medical Image Processing with Spiking Neural P Systems.- Chapter 10. More Applications of Spiking Neural P Systems.- Part III. Implementations of Spiking Neural P Systems.- Chapter 11. Software Simulations of Spiking Neural P Systems.- Chapter 12. Hardware Simulations of Spiking Neural P Systems.