Membrane Computing Models: Implementations

by Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

2021 · Springer Nature

The theoretical basis of membrane computing was established in the early 2000s with fundamental research into the computational power, complexity aspects and relationships with other (un)conventional computing paradigms. Although this core theoretical research has continued to grow rapidly and vigorously, another area of investigation has since been added, focusing on the applications of this model in many areas, most prominently in systems and synthetic biology, engineering optimization, power system fault diagnosis and mobile robot controller design. The further development of these applications and their broad adoption by other researchers, as well as the expansion of the membrane computing modelling paradigm to other applications, call for a set of robust, efficient, reliable and easy-to-use tools supporting the most significant membrane computing models. This work provides comprehensive descriptions of such tools, making it a valuable resource for anyone interested in membrane computing models.

Spiking Neural P Systems

by Gexiang Zhang, Sergey Verlan, Tingfang Wu, Francis George C. Cabarle, Jie Xue, David Orellana-Martín, Jianping Dong, Luis Valencia-Cabrera, Mario J. Pérez-Jiménez

2024 · Springer Nature

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.