Skip to main content
SpringerLink
Log in

Weighted coupled neural P systems with inhibitory rules and multiple channels

  • Research Paper
  • Published:
Journal of Membrane Computing Aims and scope Submit manuscript

Abstract

Coupled neural P systems (CNP systems) a model for computing, which is abstracted by the neuronal model of the mammalian visual cortex developed by Eckhorn. To improve the model’s controllability and consistency with biological facts, we assign different synaptic channels to indicate different channels which are used to transmit spikes, mimic the action of inhibitory synapses by establishing inhibitory regulations, introduce weights to synapse channels to describe the number of that between neurons, and propose weighted coupled neural P systems with inhibitory rules and multiple channels (WCNP–MCIR systems). In addition, the computational power of WCNP–MCIR systems is investigated. The Turing universal of the WCNP–MCIR systems as number generation and acceptation equipment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Data availability

No data sets were generated or analysed during the current study.

References

  1. Song, B., Zeng, X., & Rodríguez-Patón, A. (2021). Monodirectional tissue P systems with channel states. Information Sciences, 546, 206–219. https://doi.org/10.1016/j.ins.2020.08.030

    Article  MathSciNet  Google Scholar 

  2. Luo, Y., Zhao, Y., & Chen, C. (2020). Homeostasis tissue-like P systems. IEEE Transactions on NanoBioscience, 20(1), 126–136. https://doi.org/10.1109/TNB.2020.3025921

    Article  Google Scholar 

  3. Yang, Q., Xiong, X., Peng, H., Wang, J., & Song, X. (2023). Nonlinear spiking neural P systems with multiple channels. Theoretical Computer Science, 965, 113979. https://doi.org/10.1016/j.tcs.2023.113979

    Article  MathSciNet  Google Scholar 

  4. Song, T., Zheng, P., Wong, M. D., Jiang, M., & Zeng, X. (2019). On the computational power of asynchronous axon membrane systems. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(5), 696–704. https://doi.org/10.1109/TETCI.2019.2907724

    Article  Google Scholar 

  5. Wu, T., & Pan, L. (2020). The computation power of spiking neural P systems with polarizations adopting sequential mode induced by minimum spike number. Neurocomputing, 401, 392–404. https://doi.org/10.1016/j.neucom.2020.03.095

    Article  Google Scholar 

  6. Lv, Z., Yang, Q., Peng, H., Song, X., & Wang, J. (2021). Computational power of sequential spiking neural P systems with multiple channels. Journal of Membrane Computing, 3, 270–283. https://doi.org/10.1007/s41965-021-00089-9

    Article  MathSciNet  Google Scholar 

  7. Bao, T., Yang, Q., Peng, H., Luo, X., Wang, J., & Song, X. (2021). Computational power of sequential dendrite P systems. Theoretical Computer Science, 893, 133–145. https://doi.org/10.1016/j.tcs.2021.08.008

    Article  MathSciNet  Google Scholar 

  8. Zhang, X., Zhang, G., Paul, P., Zhang, J., Wu, T., Fan, S., & Xiong, X. (2021). Dissolved gas analysis for transformer fault based on learning spiking neural P system with belief AdaBoost. International Journal of Unconventional Computing. https://doi.org/10.1016/j.epsr.2011.09.012

    Article  Google Scholar 

  9. Zhu, M., Yang, Q., Dong, J., Zhang, G., Gou, X., Rong, H., Paul, P., & Neri, F. (2021). An adaptive optimization spiking neural P system for binary problems. International Journal of Neural Systems, 31(1), 2050054. https://doi.org/10.1142/S0129065720500549

    Article  Google Scholar 

  10. Zhang, Y., Yang, Q., Liu, Z., Peng, H., & Wang, J. (2023). A prediction model based on gated nonlinear spiking neural systems. International Journal of Neural Systems, 33(6), 2350029. https://doi.org/10.1142/S0129065723500296

    Article  Google Scholar 

  11. Guo, P., Quan, C., & Ye, L. (2019). UPSimulator: A general P system simulator. Knowledge-Based Systems, 170, 20–25. https://doi.org/10.1016/j.knosys.2019.01.013

    Article  Google Scholar 

  12. Zhang, T., Xu, F., & Wu, T. (2020). A software tool for spiking neural P systems. Romanian Journal of Information Science and Technology, 23(1), 84–92. https://doi.org/10.1007/s11047-008-9083-y

    Article  Google Scholar 

  13. Valencia-Cabrera, L., & Song, B. (2020). Tissue P systems with promoter simulation with MeCoSim and P-lingua framework. Journal of Membrane Computing, 2(2), 95–107. https://doi.org/10.1007/s41965-020-00037-z

    Article  MathSciNet  Google Scholar 

  14. Díaz-Pernil, D., Gutiérrez-Naranjo, M. A., & Peng, H. (2019). Membrane computing and image processing: A short survey. Journal of Membrane Computing, 1(1), 58–73. https://doi.org/10.1007/s41965-018-00002-x

    Article  MathSciNet  Google Scholar 

  15. Li, B., Peng, H., & Wang, J. (2021). A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images. Signal Processing, 178, 107793. https://doi.org/10.1016/j.sigpro.2020.107793

    Article  Google Scholar 

  16. Li, B., Peng, H., Wang, J., & Huang, X. (2020). Multi-focus image fusion based on dynamic threshold neural P systems and surfacelet transform. Knowledge-Based Systems, 196, 105794. https://doi.org/10.1016/j.knosys.2020.105794

    Article  Google Scholar 

  17. Reina-Molina, R., Díaz-Pernil, D., Real, P., & Berciano, A. (2015). Membrane parallelism for discrete Morse theory applied to digital images. Applicable Algebra in Engineering, Communication and Computing, 26(1), 49–71. https://doi.org/10.1007/s00200-014-0246-z

    Article  MathSciNet  Google Scholar 

  18. Zhang, G., Gheorghe, M., & Li, Y. (2012). A membrane algorithm with quantum-inspired subalgorithms and its application to image processing. Natural Computing, 11(4), 701–717. https://doi.org/10.1007/s11047-012-9320-2

    Article  MathSciNet  Google Scholar 

  19. Cai, Y., Mi, S., Yan, J., Peng, H., Luo, X., Yang, Q., & Wang, J. (2022). An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Information Sciences, 587, 473–484. https://doi.org/10.1016/j.ins.2021.12.058

    Article  Google Scholar 

  20. Li, B., Peng, H., Luo, X., Wang, J., Song, X., Pérez-Jiménez, M. J., & Riscos-Núñez, A. (2021). Medical image fusion method based on coupled neural P systems in nonsubsampled shearlet transform domain. International Journal of Neural Systems, 31(1), 2050050. https://doi.org/10.1142/S0129065720500501

    Article  Google Scholar 

  21. Xue, J., Ren, L., Song, B., Guo, Y., Lu, J., Liu, X., Gong, G., & Li, D. (2023). Hypergraph-based numerical neural-like P systems for medical image segmentation. IEEE Transactions on Parallel and Distributed Systems, 34(4), 1202–1214. https://doi.org/10.1109/TPDS.2023.3240174

    Article  Google Scholar 

  22. Song, T., Pang, S., Hao, S., Rodríguez-Patón, A., & Zheng, P. (2019). A parallel image skeletonizing method using spiking neural P systems with weights. Neural Processing Letters, 50, 1485–1502. https://doi.org/10.1007/s11063-018-9947-9

    Article  Google Scholar 

  23. Liu, M., Zhao, F., Jiang, X., Zhang, H., & Zhou, H. (2022). Parallel binary image cryptosystem via spiking neural networks variants. International Journal of Neural Systems, 32(8), 2150014. https://doi.org/10.1142/S0129065721500143

    Article  Google Scholar 

  24. Bie, D., Gutiérrez-Naranjo, M. A., Zhao, J., & Zhu, Y. (2019). A membrane computing framework for self-reconfigurable robots. Natural Computing, 18(3), 635–646. https://doi.org/10.1007/s11047-018-9702-1

    Article  MathSciNet  Google Scholar 

  25. Buiu, C., & Florea, A. G. (2019). Membrane computing models and robot controller design, current results and challenges. Journal of Membrane Computing, 1(4), 262–269. https://doi.org/10.1007/s41965-019-00029-8

    Article  MathSciNet  Google Scholar 

  26. Liu, W., Wang, T., Zang, T., Huang, Z., Wang, J., Huang, T., Wei, X., Li, C., et al. (2020). A fault diagnosis method for power transmission networks based on spiking neural P systems with self-updating rules considering biological apoptosis mechanism. Complexity, 2020, 18. https://doi.org/10.1155/2020/2462647

    Article  Google Scholar 

  27. Liu, Y., Chen, Y., Paul, P., Fan, S., Ma, X., & Zhang, G. (2021). A review of power system fault diagnosis with spiking neural P systems. Applied Sciences-Basel, 11(10), 4376. https://doi.org/10.3390/app11104376

    Article  Google Scholar 

  28. Peng, H., Wang, J., Ming, J., Shi, P., Pérez-Jiménez, M. J., Yu, W., & Tao, C. (2017). Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems. IEEE Transactions on Smart Grid, 9(5), 4777–4784. https://doi.org/10.1109/TSG.2017.2670602

    Article  Google Scholar 

  29. Rong, H., Ge, M., Zhang, G., & Zhu, M. (2018). An approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. International Journal of Computers Communications & Control, 13(4), 521–536. https://doi.org/10.15837/IJCCC.2018.4.3220

    Article  Google Scholar 

  30. Rong, H., Yi, K., Zhang, G., Dong, J., Paul, P., & Huang, Z. (2019). Automatic implementation of fuzzy reasoning spiking neural P systems for diagnosing faults in complex power systems. Complexity. https://doi.org/10.1155/2019/2635714

    Article  Google Scholar 

  31. Wang, J., Peng, H., Yu, W., Ming, J., Pérez-Jiménez, M. J., Tao, C., & Huang, X. (2019). Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks. Engineering Applications of Artificial Intelligence, 82, 102–109. https://doi.org/10.1016/j.engappai.2019.03.014

    Article  Google Scholar 

  32. Wang, T., Wei, X., Huang, T., Wang, J., Peng, H., Pérez-Jiménez, M. J., & Valencia-Cabrera, L. (2019). Modeling fault propagation paths in power systems: A new framework based on event SNP systems with neurotransmitter concentration. IEEE Access, 7, 12798–12808. https://doi.org/10.1109/ACCESS.2019.2892797

    Article  Google Scholar 

  33. Wang, T., Wei, X., Wang, J., Huang, T., Peng, H., Song, X., Cabrera, L. V., & Pérez-Jiménez, M. J. (2020). A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies. Engineering Applications of Artificial Intelligence, 92, 103680. https://doi.org/10.1016/j.engappai.2020.103680

    Article  Google Scholar 

  34. Yin, X., Liu, X., Sun, M., Dong, J., & Zhang, G. (2022). Fuzzy reasoning numerical spiking neural P systems for induction motor fault diagnosis. Entropy, 24(10), 1385. https://doi.org/10.3390/e24101385

    Article  Google Scholar 

  35. Song, T., Zeng, X., Zheng, P., Jiang, M., & Rodriguez-Paton, A. (2018). A parallel workflow pattern modeling using spiking neural P systems with colored spikes. IEEE Transactions on NanoBioscience, 17(4), 474–484. https://doi.org/10.1109/TNB.2018.2873221

    Article  Google Scholar 

  36. Zhang, G., Pérez-Jiménez, M. J., & Gheorghe, M. (2017). Real-life applications with membrane computing (Vol. 25). Springer. https://doi.org/10.1007/978-3-319-55989-6

    Book  Google Scholar 

  37. Zhang, X., Zeng, X., Luo, B., & Xu, J. (2012). Several applications of spiking neural P systems with weights. Journal of Computational and Theoretical Nanoscience, 9(6), 769–777. https://doi.org/10.1166/jctn.2012.2094

    Article  Google Scholar 

  38. Ionescu, M., Păun, G., & Yokomori, T. (2006). Spiking neural P systems. Fundamenta Informaticae, 71(2–3), 279–308.

    MathSciNet  Google Scholar 

  39. Pan, L., Wang, J., & Hoogeboom, H. J. (2012). Spiking neural P systems with astrocytes. Neural Computation, 24(3), 805–825. https://ieeexplore.ieee.org/abstract/document/6797273

  40. Cabarle, F. G. C., Adorna, H. N., Pérez-Jiménez, M. J., & Song, T. (2015). Spiking neural P systems with structural plasticity. Neural Computing and Applications, 26(8), 1905–1917. https://doi.org/10.1007/s00521-015-1857-4

    Article  Google Scholar 

  41. 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. (2019). Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity. Journal of Membrane Computing, 1(3), 145–160. https://doi.org/10.1007/s41965-019-00020-3

    Article  MathSciNet  Google Scholar 

  42. Pan, L., Păun, G., & Pérez-Jiménez, M. J. (2011). Spiking neural P systems with neuron division and budding. Science China Information Sciences, 54(8), 1596–1607. https://doi.org/10.1007/s11432-011-4303-y

    Article  MathSciNet  Google Scholar 

  43. Pan, L., & Păun, G. (2009). Spiking neural P systems with anti-spikes. International Journal of Computers Communications & Control, 4(3), 273–282. https://doi.org/10.15837/ijccc.2009.3.2435

    Article  Google Scholar 

  44. Song, T., Rodríguez-Patón, A., Zheng, P., & Zeng, X. (2017). Spiking neural P systems with colored spikes. IEEE Transactions on Cognitive and Developmental Systems, 10(4), 1106–1115. https://doi.org/10.1109/TCDS.2017.2785332

    Article  Google Scholar 

  45. Wu, T., Pan, L., Yu, Q., & Tan, K. C. (2020). Numerical spiking neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2443–2457. https://doi.org/10.1109/TNNLS.2020.3005538

    Article  MathSciNet  Google Scholar 

  46. Yin, X., Liu, X., Sun, M., & Ren, Q. (2021). Novel numerical spiking neural P systems with a variable consumption strategy. Processes, 9(3), 549. https://doi.org/10.3390/pr9030549

    Article  Google Scholar 

  47. Wu, T., Zhang, L., & Pan, L. (2021). Spiking neural P systems with target indications. Theoretical Computer Science, 862, 250–261. https://doi.org/10.1016/j.tcs.2020.07.016

    Article  MathSciNet  Google Scholar 

  48. Song, X., Valencia-Cabrera, L., Peng, H., Wang, J., & Pérez-Jiménez, M. J. (2021). Spiking neural P systems with delay on synapses. International Journal of Neural Systems, 31(1), 2050042. https://doi.org/10.1142/S0129065720500422

    Article  Google Scholar 

  49. Jiang, S., Fan, J., Liu, Y., Wang, Y., & Xu, F. (2020). Spiking neural P systems with polarizations and rules on synapses. Complexity, 2020, 1–12. https://doi.org/10.1155/2020/8742308

    Article  Google Scholar 

  50. Alhazov, A., Freund, R., Ivanov, S., Oswald, M., & Verlan, S. (2018). Extended spiking neural P systems with white hole rules and their red-green variants. Natural Computing, 17(2), 297–310. https://doi.org/10.1007/s11047-017-9649-7

    Article  MathSciNet  Google Scholar 

  51. Pan, L., & Zeng, X. (2011). Small universal spiking neural P systems working in exhaustive mode. IEEE Transactions on NanoBioscience, 10(2), 99–105. https://doi.org/10.1109/TNB.2011.2160281

    Article  Google Scholar 

  52. Wu, T., & Jiang, S. (2021). Spiking neural P systems with a flat maximally parallel use of rules. Journal of Membrane Computing, 3(3), 221–231. https://doi.org/10.1007/s41965-020-00069-5

    Article  MathSciNet  Google Scholar 

  53. Peng, H., Yang, J., Wang, J., Wang, T., Sun, Z., Song, X., Luo, X., & Huang, X. (2017). Spiking neural P systems with multiple channels. Neural Networks, 95, 66–71. https://doi.org/10.1016/j.neunet.2017.08.003

    Article  Google Scholar 

  54. Pan, L., Zeng, X., Zhang, X., & Jiang, Y. (2012). Spiking neural P systems with weighted synapses. Neural Processing Letters, 35(1), 13–27. https://doi.org/10.1007/s11063-011-9201-1

    Article  Google Scholar 

  55. Peng, H., Li, B., Wang, J., Song, X., Wang, T., Valencia-Cabrera, L., Pérez-Hurtado, I., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2020). Spiking neural P systems with inhibitory rules. Knowledge-Based Systems, 188, 105064. https://doi.org/10.1016/j.knosys.2019.105064

    Article  Google Scholar 

  56. Peng, H., & Wang, J. (2018). Coupled neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1672–1682. https://doi.org/10.1109/TNNLS.2018.2872999

    Article  MathSciNet  Google Scholar 

  57. Arora, S., & Barak, B. (2009). Computational complexity: A modern approach. Cambridge University Press. https://doi.org/10.1017/CBO9780511804090

    Book  Google Scholar 

  58. Macababayao, I. C. H. (2022). Normal forms for spiking neural P systems and some of its variants. Information Sciences, 595, 344–363. https://doi.org/10.1016/j.ins.2022.03.002

    Article  Google Scholar 

  59. Pan, L., & Pǎun, G. (2010). Spiking neural P systems: An improved normal form. Theoretical Computer Science, 411(6), 906–918. https://doi.org/10.1016/j.tcs.2009.11.010

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural science Foundation of china (Nos. 61806114, 61472231, 61876101, 61602282, 61402187, 61502283, 61802234 and 61703251),the China Postdoctoral science Foundation (Nos. 2018M642695 and 2019T120607) and the Shandong Province Natural ScienceFoundation (No. ZR2023MFO79).

Author information

Authors and Affiliations

  1. Business School, Shandong Normal University, Jinan, 250358, Shandong, China

    Yuzhen Zhao, Mingyuan Wang, Qihui Miao & Zhen Yang

Authors
  1. Yuzhen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingyuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qihui Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ provided the idea, reviewed the manuscript. YZ and ZY revised the manuscript and wrote the point-to-point response. MW and QM wrote the main manuscript text.

Corresponding author

Correspondence to Yuzhen Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Y., Wang, M., Miao, Q. et al. Weighted coupled neural P systems with inhibitory rules and multiple channels. J Membr Comput 6, 67–81 (2024). https://doi.org/10.1007/s41965-024-00143-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41965-024-00143-2

Keywords

Search

Navigation