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Spiking neural P systems and kernel P systems
Gheorghe, Marian, Ipate, F. Kannan, K. Konur, Savas, Kuppusamy, L. Lefticaru, Raluca, Mahendran, A. Niculescu, M.I.
Gheorghe, Marian,
Ipate, F.
Kannan, K.
Konur, Savas,
Kuppusamy, L.
Lefticaru, Raluca,
Mahendran, A.
Niculescu, M.I.
Publication Date
2025-12-01
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© 2025 The Author(s). This is the Author Accepted Manuscript of the article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0) in accordance with the University of Bradford Rights Retention Policy.
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openAccess
Accepted for publication
2025-07-31
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Abstract
Spiking neural P systems represent one the most dynamic and active research area of membrane computing, with many variants being defined and investigated, and a broad spectrum of applications reported. Often, each newly introduced spiking neural P system is compared with others to reveal its potential. However, there is a need to compare them from another perspective, when they are all represented with the same instrumentation within a given framework and where quantitative metrics can precisely describe the complexity of their representations. In this paper a selection of the best known and investigated classes of spiking neural P systems are mapped into kernel P system models and different encodings of the computation results and strategies of using the rules are investigated. Complexity metrics associated with the translation processes are assessed. Specific methods to check model correctness and to provide test sets for implementations are used for an example in order to illustrate the connection between these P systems.
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Accepted manuscript
Citation
Gheorghe M, Ipate F, Kannan, K et al (2025) Spiking neural P systems and kernel P systems. Journal of Membrane Computing. 7: 437–459.
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Article