Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-22T11:16:31.708Z Has data issue: false hasContentIssue false

Recurrent ring dynamics in two-dimensional excitable cellular automata

Published online by Cambridge University Press:  14 July 2016

Janko Gravner*
Affiliation:
University of California
*
Postal address: Mathematics Department, University of California, Davis, CA 95616, USA. Email address: [email protected].

Abstract

The Greenberg–Hastings model (GHM) is a simple cellular automaton which emulates two properties of excitable media: excitation by contact and a refractory period. We study two ways in which external stimulation can make ring dynamics in the GHM recurrent. The first scheme involves the initial placement of excitation centres which gradually lose strength, i.e. each time they become inactive (and then stay inactive forever) with probability 1 − pf. In this case, the density of excited sites must go to 0; however, their long–term connectivity structure undergoes a phase transition as pf increases from 0 to 1. The second proposed rule utilizes continuous nucleation in that new rings are started at every rested site with probability ps. We show that, for small ps, these dynamics make a site excited about every ps−1/3 time units. This result yields some information about the asymptotic shape of a closely related random growth model.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1999 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This work was partly supported by the research grant J1-6157-0101-94 from Slovenia's Ministry of Science and Technology.

References

Billingsley, P. (1986). Probability and Measure, 2nd edn. Wiley, New York.Google Scholar
Cox, J. T., Gandolfi, A., Griffin, P. S., and Kesten, H. (1993). Greedy lattice animals I: upper bounds. Ann. Appl. Prob. 3, 11511169.Google Scholar
Durrett, R., and Griffeath, D. (1993). Asymptotic behavior of excitable cellular automata. Experiment. Math. 2, 184208.CrossRefGoogle Scholar
Durrett, R., and Neuhauser, C. (1991). Epidemic with recovery in d=2. Ann. Appl. Prob. 1, 189206.CrossRefGoogle Scholar
Durrett, R., and Steif, J. E. (1991). Some rigorous results for the Greenberg–Hastings model. J. Theoret. Prob. 4, 669690.CrossRefGoogle Scholar
Ekhaus, M., and Gray, L. (1999). Convergence to equilibrium and a strong law for the motion of restricted interfaces. Preprint.Google Scholar
Fisch, R., Gravner, J., and Griffeath, D. (1991). Threshold-range scaling of excitable cellular automata. Statist. Comput. 1, 2339.Google Scholar
Fisch, R., Gravner, J., and Griffeath, D. (1993). Metastability in the Greenberg–Hastings model. Ann. Appl. Prob. 3, 935967.Google Scholar
Fraser, S., and Kapral, R. (1986). Ring dynamics and percolation in an excitable medium. J. Chem. Phys. 85, 56825688.CrossRefGoogle Scholar
Gravner, J. (1996). Percolation times in two dimensional models for excitable media. Electron. J. Prob. 1, 119.Google Scholar
Gravner, J. (1996). Cellular automata models of ring dynamics. Int. J. Mod. Phys. C 7, 863871.CrossRefGoogle Scholar
Gravner, J., and Griffeath, D. (1996). First passage times for threshold growth dynamics on ℤ2 . Ann. Prob. 24, 17521778.Google Scholar
Greenberg, J. M., and Hastings, S. P. (1978). Spatial patterns for discrete models of diffusion in excitable media. SIAM J. Appl. Math. 34, 515523.CrossRefGoogle Scholar
Griffeath, D. (1994). Self-organization of random cellular automata: four snapshots. In Probability and Phase Transitions, ed. Grimmett, G. Kluwer, Dordrecht, pp. 4967.CrossRefGoogle Scholar
Griffeath, D. (1999). Primordial Soup Kitchen. Available at http://psoup.math.wisc.edu.Google Scholar
Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc. 58, 1330.Google Scholar
Kesten, H., and Schonmann, R. H. (1995). On some growth models with a small parameter. Prob. Theory Rel. Fields 101, 435468.Google Scholar
Liggett, T. (1985). Interacting Particle Systems. Springer, New York.Google Scholar
McDiarmid, C. (1989). On the method of bounded differences. In Surveys in Combinatorics, ed. Siemons, J. (London Math. Soc. Lecture Notes Ser. 141.) CUP, Cambridge, pp. 148188.Google Scholar
Meester, R., and Roy, R. (1996). Continuum Percolation. CUP, Cambridge.CrossRefGoogle Scholar
Penrose, M. (1996). The threshold contact process: a continuum limit. Prob. Theory Rel. Fields 104, 7795.Google Scholar
Raschman, P., Kubiček, M., and Marek, M. (1980). Waves in distributed chemical systems: experiments and computations. In New Approaches to Nonlinear Problems in Dynamics, ed. Holmes, P. SIAM, Philadelphia, PA, pp. 271288.Google Scholar
Steif, J. (1995). Two applications of percolation to cellular automata. J. Statist. Phys. 78, 13251335.Google Scholar
Tyson, J. J., and Fife, P. C. (1980). Target patterns in a realistic model of the Belousov–Zhabotinskii reaction. J. Chem. Phys. 73, 22242237.Google Scholar
Toffoli, T., and Margolus, N. (1987). Cellular Automata Machines. MIT, Cambridge, MA.CrossRefGoogle Scholar
Weimar, J. R., Tyson, J. J., and Watson, L. T. (1992). Third generation cellular automaton for modeling excitable media. Physica D 55, 328339.Google Scholar
Wiener, N., and Rosenbluth, A. (1946). The mathematical formulation of the problem of conduction of impulses in a network of connected excitable elements, specifically in cardiac muscle. Arch. Inst. Cardiol. Mexico 16, 205265.Google Scholar