Spiking Neural P Systems

• Autorzy: Ionescu M. , Paun G. , Yokomori T.
• Języki publikacji: EN

Abstrakty

EN

This paper proposes a way to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons fire and/or spike) plays an essential role. For instance, the result of a computation is the time between the moments when a specified neuron spikes. Seen as number computing devices, SN P systems are shown to be computationally complete (both in the generating and accepting modes, in the latter case also when restricting to deterministic systems). If the number of spikes present in the system is bounded, then the power of SN P systems falls drastically, and we get a characterization of semilinear sets. A series of research topics and open problems are formulated.

Słowa kluczowe

EN

spiking neurons; membrane computing; turing computability; semilinear set

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2006
• Tom: Vol. 71, nr 2,3
• Strony: 279--308
• Opis fizyczny: tab., wykr., bibliogr. 16 poz.

Twórcy

autor

Ionescu M.

autor

Paun G.

autor

Yokomori T.

• Research Group on Mathematical Linguistics Rovira I Virgili University Pl. Imperial Tarraco 1, 43005 Tarragona, Spain,

Bibliografia

• [1] B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter: Molecular Biology of the Cell, 4th ed. Garland Science, New York, 2002.
• [2] W. Gerstner, W Kistler: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge Univ. Press, 2002.
• [3] O.H. Ibarra, S. Woodworth, H.-C. Yen, Z. Dang: On symport/antiport P systems and semilinear sets. Membrane Computing, International Workshop, WMC6, Vienna, Austria, July 2005, Selected and Invited Papers (R. Freund, Gh. P˘aun, G. Rozenberg, A. Salomaa, eds.), LNCS 3850, Springer-Verlag, Berlin, 2006, 255-273.
• [4] Z. Kemp, A. Kowalczyk: Incorporating the temporal dimension in a GIS. Chapter 7 in Innovations in GIS 1, Taylor and Francis, London, 1994.
• [5] V. Lum, P. Dadam, R. Erbe, J. Guenaver, P. Pistor, G.Walch, H.Werner, J.Woodfill: Designing dbms support for the temporal dimension. In Proceedings of SIGMOD'84 Conference, 1984, 115-130.
• [6] W. Maass: Computing with spikes. Special Issue on Foundations of Information Processing of TELEMATIK, 8, 1 (2002), 32-36.
• [7] W. Maass, C. Bishop, eds.: Pulsed Neural Networks, MIT Press, Cambridge, 1999.
• [8] C. Martin-Vide, Gh. P˘aun, J. Pazos, A. Rodr´ıguez-Patón: A new class of symbolic abstract neural nets: Tissue P systems. In Proceedings of COCOON 2002, Singapore, LNCS 2387, Springer-Verlag, Berlin, 2002, 290-299.
• [9] M. Minsky: Computation - Finite and Infinite Machines. Prentice Hall, Englewood Cliffs, NJ, 1967.
• [10] Gh. Pǎun: Computing with membranes. Journal of Computer and System Sciences, 61 (2000), 108-143 (also TUCS Report 208, November 1998, http://www.tucs.fi).
• [11] Gh. Pǎaun: Membrane Computing - An Introduction. Springer-Verlag, Berlin, 2002.
• [12] Gh. Pǎun, Y. Sakakibara, T. Yokomori: P systems on graphs of restricted forms. Publicationes Mathematicae Debrecen, 60 (2002), 635-660.
• [13] G. Rozenberg, A. Salomaa, eds.: Handbook of Formal Languages, 3 volumes. Springer-Verlag, Berlin, 1997.
• [14] C. Teuscher: Turing's Connectionism. An Investigation of Neural Network Architectures. Springer-Verlag, London, 2002.
• [15] S. Yu: The time dimension of computation models. Technical Report 549, Computer Sci. Dept., Univ. Of Western Ontario, London-Ontario, Canada, 2000.
• [16] The P Systems Web Page:http://psystems.disco.unimib.it