Abstract
Virus machines are unconventional and bio-inspired models of computation based on the transmission of viruses among hosts. Virus machines are known to be computationally complete (they are algorithms), able to solve computationally hard problems. In this work we present a novel matrix representation for virus machines. Discrete structures such as vectors and matrices are useful in many technical domains, both in theory and practice. The hosts, number of viruses, and the instructions to control virus transmission are represented as vectors and matrices. In this way the computations of virus machines can be described by linear algebra operations. We also use our matrix representation to show invariants, helpful in formal verifications, of such machines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aboy, B.C.D., et al.: Optimizations in CuSNP Simulator for spiking neural P systems on CUDA GPUs. In: 2019 International Conference on High Performance Computing and Simulation (HPCS), pp. 535–542. IEEE (2019)
Adorna, H.N.: Matrix representations of spiking neural P systems: Revisited, pp. 227–247. ar**v preprint ar**v:2211.15156 (2022)
Martínez-del Amor, M.Á.: A vision for the next generation of P system simulators. Bull. Int. Membr. Comput. Soc. 12, 83–88 (2021)
Martínez-del Amor, M.Á., Orellana-Martín, D., Pérez-Hurtado, I., Cabarle, F.G.C., Adorna, H.N.: Simulation of spiking neural P systems with sparse matrix-vector operations. Processes 9(4), 690 (2021)
Ramírez-de Arellano, A., Orellana-Martín, D., Pérez-Jiménez, M.J.: Basic arithmetic calculations through virus-based machines. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds.) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence, pp. 403–412. Springer International Publishing, Cham (2022)
Ramírez-de Arellano, A., Orellana-Martín, D., Pérez-Jiménez, M.J.: Using virus machines to compute pairing functions. Int. J. Neural Syst. 33(05), 2350023 (2023). https://doi.org/10.1142/s0129065723500235
Cabarle, F.G.C., Adorna, H.N.: On Structures and Behaviors of Spiking Neural P Systems and Petri Nets. In: Csuhaj-Varjú, E., Gheorghe, M., Rozenberg, G., Salomaa, A., Vaszil, G. (eds.) CMC 2012. LNCS, vol. 7762, pp. 145–160. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36751-9_11
Carandang, J.P., Cabarle, F.G.C., Adorna, H.N., Hernandez, N.H.S., Martínez-del Amor, M.Á.: Handling non-determinism in spiking neural P systems: Algorithms Simul. Fundam. Inf. 164(2-3), 139–155 (2019)
Chen, X., Pérez-Jiménez, M.J., Valencia-Cabrera, L., Wang, B., Zeng, X.: Computing with viruses. Theoret. Comput. Sci. 623, 146–159 (2016). https://doi.org/10.1016/j.tcs.2015.12.006
Gheorghe, M., Lefticaru, R., Konur, S., Niculescu, I.M., Adorna, H.N.: Spiking neural P systems: matrix representation and formal verification. J. Membr. Comput. 3(2), 133–148 (2021)
Gungon, R.V., et al.: GPU implementation of evolving spiking neural P systems. Neurocomputing 503, 140–161 (2022)
Jimenez, Z.B., Cabarle, F.G.C., de la Cruz, R.T.A., Buño, K.C., Adorna, H.N., Hernandez, N.H.S., Zeng, X.: Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity. J. Membr. Comput. 1, 145–160 (2019)
Leporati, A., Mauri, G., Zandron, C.: Spiking neural P systems: main ideas and results. Nat. Comput. 21(4), 629–649 (2022)
Pérez-Jiménez, M.J., Ramírez-de-Arellano, A., Orellana-Martín, D.: Attacking cryptosystems by means of virus machines. Sci. Rep.13(1), 21831 (2023). https://doi.org/10.1038/s41598-023-49297-6
Ramírez-de-Arellano, A., Orellana-Martín, D., Pérez-Jiménez, M.J.: Generating, computing and recognizing with virus machines. Theoret. Comput. Sci. 972, 114077 (2023). https://doi.org/10.1016/j.tcs.2023.114077
Rozenberg, G., Bäck, T., Kok, J.N.: Handbook of natural computing, vol. 4. Springer Berlin, Heidelberg, Heidelberg (2012). https://doi.org/10.1007/978-3-540-92910-9
Valencia-Cabrera, L., Pérez-Hurtado, I., Martínez-del Amor, M.Á.: Simulation challenges in membrane computing. J. Membr. Comput. 2(4), 392–402 (2020)
Willey, J., Sherwood, L., Woolverton, C., Prescott, L.M.: Prescott’s Principles of Microbiology. McGraw-Hill, Woolverton, Boston (2011)
Zeng, X., Adorna, H., Martínez-del-Amor, M., Pan, L., Pérez-Jiménez, M.J.: Matrix Representation of Spiking Neural P Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 377–391. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-18123-8_29
Acknowledgements
The research described in this work was supported by the Zhejiang Lab BioBit Program (Grant No. 2022BCF05). F.G.C. Cabarle is supported by QUAL21 008 USE project (PAIDI 2020 and FEDER 2014–2020 funds).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ramírez-de-Arellano, A., Cabarle, F.G.C., Orellana-Martín, D., Pérez-Jiménez, M.J., Adorna, H.N. (2024). Matrix Representation of Virus Machines. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-61137-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61136-0
Online ISBN: 978-3-031-61137-7
eBook Packages: Computer ScienceComputer Science (R0)