×

Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. (English) Zbl 1428.68155

Summary: In biological nervous systems, the operation of interacting neurons depends largely on the regulation from astrocytes. Inspired by this biological phenomenon, spiking neural P systems, i.e. SN P systems, with astrocyte-like control were proposed and were proven to have “Turing completeness” as computing models. In this work, the application of such systems for creating logical operators is investigated. Specifically, it is obtained in a constructive way that SN P systems with astrocyte-like control can synthesize the operations of Boolean logic gates, i.e. AND, OR, NOT, NOR, XOR and NAND gates. The resulting systems are simple and homogeneous, which means only one type of neuron with a unique spiking rule is used. With these neural-like logic gates, more complex Boolean circuits with cascade connections can be constructed. As such, they can be used to implement finite computing devices, such as the finite transducers. These results demonstrate a novel method of constructing logic circuits that work in a neural-like manner, as well as shed some lights on potential directions of designing neural circuits theoretically.

MSC:

68Q07 Biologically inspired models of computation (DNA computing, membrane computing, etc.)
68Q10 Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.)
94C11 Switching theory, applications of Boolean algebras to circuits and networks
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Bezzi, P.; Carmignoto, G.; Pasti, L., Prostaglandins stimulate calcium-dependent glutamate release in astrocytes, Nature, 391, 6664, 281-285 (1998)
[2] Binder, A.; Freund, R.; Oswald, M.; Vock, L., Extended spiking neural P systems with excitatory and inhibitory astrocytes, Proceedings of the 8th WSEAS International Conference on Evolutionary Computing, Vancouver, British Columbia, Canada, June 19-21 (2007)
[3] Cabarle, F. G.C.; Adorna, H. N.; Perez-Jimenez, M. J.; Song, T., Spiking neural p systems with structural plasticity, Neural Comput. Appl., 26, 1905-1917 (2015)
[4] Campuzano, F.; Garcia-Valverde, T.; Botia, J. A.; Serrano, E., Generation of human computational models with machine learning, Inf. Sc., 293, 97-114 (2015)
[5] Cavaliere, M.; Ibarra, O. H.; Păun, G.; Egecioglu, O.; Ionescu, M.; Woodworth, S., Asynchronous spiking neural P systems, Theor. Comput. Sci., 410, 24, 2352-2364 (2009) · Zbl 1168.68014
[6] Ceterchi, R.; Sburlan, D., Simulating Boolean circuits with P systems, Membrane Computing, Volume 2933 of the series Lecture Notes in Computer Science, 104-122 (2004) · Zbl 1202.68187
[7] Chen, Z.; Zhang, P.; Wang, X.; Shi, X.; Wu, T.; Zheng, P., A computational approach for nuclear export signals identification using spiking neural P systems, Neural Comput. Appl. (2016)
[8] Couso, I.; Sanchez, L., Machine learning models, epistemic set-valued data and generalized loss functions: an encompassing approach, Inf. Sci., 358, 129-150 (2016) · Zbl 1427.68263
[9] Erhan, D.; Bengio, Y.; Courville, A., Why does unsupervised pre-training help deep learning, J. Mach. Learn. Res., 11, 625-660 (2010) · Zbl 1242.68219
[10] Gao, R.; Wang, Y.; Lai, J., Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks, Inf. Sci., 367-368, 449-462 (2016) · Zbl 1429.93179
[11] Gheorghe, M.; Konur, S.; Ipate, F., Kernel P systems and stochastic P systems for modelling and formal verification of genetic logic gates, Advances in Unconventional Computing, Volume 22 of the series Emergence, Complexity and Computation, 661-675 (2016)
[12] Gutierrez-Naranjo, A. M.; Leporati, A., First steps towards a CPU made of spiking neural p systems, Int. J. Comput. Commun. Control, IV, 3, 244-252 (2009)
[13] Haykin, S., Neural Networks and Learning Machines (2009), Upper Saddle River: Upper Saddle River New Jersy, USA Pearson
[14] He, R.; Tang, J.; Gong, P., P, multi-document summarization via group sparse learning, Inf. Sci., 349, 12-24 (2016) · Zbl 1398.68439
[15] Ibarra, O. H.; Păun, A.; Rodríguez-Patón, A., Sequential SNP systems based on min/max spike number, Theor. Comput. Sci., 410, 30, 2982-2991 (2009) · Zbl 1173.68020
[16] Ionescu, M.; Păun, G.; Yokomori, T., Spiking neural P systems, Fundamenta Informaticae, 71, 2, 279-308 (2006) · Zbl 1110.68043
[17] Ionescu, M.; Ishdorj, T. O., Boolean circuits and a DNA algorithm in membrane computing, Membrane Computing, Volume 3850 of the series Lecture Notes in Computer Science, 272-291 (2006) · Zbl 1135.68415
[18] Ionescu, M.; Sburlan, D., Several applications of spiking neural P systems, Comput. Inf., 27, 515-528 (2008) · Zbl 1389.68032
[19] Kasabov, N.; Capecci, E., Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Inf. Sci., 294, 565-575 (2015)
[20] Krithivasan, K.; Metta, V. P.; Garg, D., On string languages generated by spiking neural P systems with anti-spikes, Int. J. Found. Comput.Sci., 22, 01, 15-27 (2011) · Zbl 1214.68157
[21] Li, X.; Wang, Z.; Lu, W., A spiking neural system based on DNA strand displacement, J. Comput. Theor.Nanosci. (2015)
[22] Liu, X.; Li, Z.; Liu, J.; Liu, L.; Zeng, X., Implementation of arithmetic operations with time-free spiking neural p systems, IEEE Trans. Nanobiosci., 14, 6, 617-624 (2015)
[23] Maass, W., Networks of spiking neurons: the third generation of neural network models, Neural Netw., 10, 9, 1659-1671 (1997)
[24] Maass, W.; Bishop, C. M., Pulsed Neural Networks (2001), MIT press
[25] Pan, L.; Wang, J.; Hoogeboom, H. J., Spiking neural P systems with astrocytes, Neural Comput., 24, 3, 805-825 (2012) · Zbl 1238.68056
[26] Metta, V. P.; Krithivasan, K.; Garg, D., Some characteristics of spiking neural P systems with anti-spikes, Proceedings of the 11th International Conference on Membrane Computing, Jena, Germany, 291-303 (2010)
[27] Pan, L.; Păun, G., Spiking neural P systems with anti-spikes, Int. J. Comput. Commun. Control, IV, 3, 273-282 (2009)
[28] Pan, L.; Zeng, X.; Zhang, X., Time-free spiking neural P systems, Neural Comput., 23, 5, 1320-1342 (2011) · Zbl 1216.68113
[29] Păun, G., Membrane Computing: An Introduction (2002), Springer · Zbl 1034.68037
[30] Păun, G.; Pérez-Jiménez, M. J., Spiking neural P systems: an overview, (Pazos, A. B.P.; Sierra, A. P.; Buceta, W. B., Advancing Artificial Intelligence through Biological Process Applications (2008)), 60-73
[31] Păun, G.; Rozenberg, G.; Salomaa, A., The Oxford Handbook of Membrane Computing (2010), Oxford University Press · Zbl 1237.68001
[32] Păun, G., Spiking neural P systems with astrocyte-like control, J. Universal Comput. Sci., 13, 11, 1707-1721 (2007)
[33] Peng, H.; Wang, J.; Pérez-Jiménez, M. J.; Wang, H.; Shao, J.; Wang, T., Fuzzy reasoning spiking neural P system for fault diagnosis, Inf. Sci., 235, 106-116 (2013) · Zbl 1284.68265
[34] Perea, G.; Navarrete, M.; Araque, A., Tripartite synapses: astrocytes process and control synaptic information, TrendsNeurosci., 32, 8, 421-431 (2009)
[35] Rozenberg, G.; Salomaa, A., Handbook of Formal Languages, vol. 3 (1997), Springer-Verlag: Springer-Verlag Berlin · Zbl 0866.68057
[36] Schmidhuber, J., Deep learning in neural networks: an overview, Neural Netw., 61, 85-117 (2015)
[37] Song, T.; Pan, L.; Jiang, K.; Song, B.; Chen, W., Normal forms for some classes of sequential spiking neural P systems, IEEE Trans. NanoBiosci., 12, 3, 255-264 (2013)
[38] Song, T.; Pan, L.; Păun, G., Spiking neural P systems with rules on synapses, Theor. Comput. Sci., 529, 82-95 (2014) · Zbl 1358.68104
[39] Song, T.; Pan, L., Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy, IEEE Trans. Nanobiosci., 14, 1, 38-44 (2015)
[40] Song, T.; Pan, L., Spiking neural P systems with rules on synapses working in maximum spiking strategy, IEEE Trans. Nanobiosci., 14, 4, 465-477 (2015)
[41] Song, T.; Pan, L., Spiking neural p systems with request rules, Neurocomputing, 193, 193-200 (2016)
[42] Song, T.; Pan, L.; Păun, G., Asynchronous spiking neural P systems with local synchronization, Inf. Sci., 219, 197-207 (2012) · Zbl 1293.68122
[43] Shi, X.; Wang, Z.; Deng, C.; Song, T.; Pan, L.; Chen, Z., A novel bio-sensor based on DNA strand displacement, PLOS ONE, 9, 10 (2014)
[44] Shi, X.; Wu, X.; Song, T.; Li, X., Construction of DNA nanotubes with controllable diameters and patterns by using hierarchical DNA sub-tiles, Nanoscale (2016)
[45] Wang, J.; Shi, P.; Peng, H.; Pérez-Jiménez, M. J.; Wang, T., Weighted fuzzy spiking neural P systems, Fuzzy Syst. IEEE Trans., 21, 2, 209-220 (2013)
[46] Wang, J.; Peng, H., Adaptive fuzzy spiking neural P systems for fuzzy inference and learning, Int. J. Comput. Math., 90, 4, 857-868 (2013) · Zbl 1286.68147
[47] Wang, T.; Zhang, G.; Pérez-Jiménez, M. J., Fuzzy membrane computing: theory and applications, Int. J. Comput. Commun. Control, 10, 6, 904-935 (2015)
[48] Wang, T.; Zhang, G.; Zhao, J.; He, Z.; Wang, J.; Pérez-Jiménez, M. J., Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems, IEEE Trans. on Power System, 30, 3, 1182-1194 (2015)
[49] Wang, X.; Song, T.; Gong, F.; Zheng, P., On the computational power of spiking neural Psystems with self-orgniaztion, Sci. Rep., 6 (2016)
[50] Wu, T.; Zhang, Z.; Păun, G., Cell-like spiking neural P systems, Theor. Comput. Sci., 623, 180-189 (2016) · Zbl 1336.68076
[51] Xie, Z.; Sun, J.; Palade, V., Evolutionary sampling: a novel way of machine learning within a probabilistic framework, Inf. Sci., 299, 262-282 (2015) · Zbl 1360.68722
[52] Zhang, G.; Rong, H.; Neri, F.; Pérez-Jiménez, M. J., An optimization spiking neural p system for approximately solving combinatorial optimization problems, Int.J.Neural Syst., 24, 05, Article 1440006 pp. (2014)
[53] Zhang, X.; Zeng, X.; Luo, B.; Pan, L., On some classes of sequential spiking neural P systems, Neural Comput., 26, 5, 974-997 (2014) · Zbl 1410.68128
[54] Zhang, J.; Zhu, Y.; Pan, Y.; Li, T., Efficient parallel boolean matrix based algorithms for computing composite rough set approximations, Inf. Sci., 329, 287-302 (2016) · Zbl 1390.68687
[55] Zhu, C.; Wang, Z.; Gao, D., Double-fold localized multiple matrixized learning machine, Information Sciences, 295, 196-220 (2015) · Zbl 1360.68731
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.