Transient Induced Global Response Synchronization

Transient Induced Global Response Synchronization

William Sulis

Collective Intelligence Laboratory, McMaster University, Canada

1 October 2016
| Citation



Synchronization has a long history in physics where it refers to the phase locking of identical oscillators. This notion has been applied in biology to such widely varying phenomena as the flashing of fireflies and the binding problem in the brain. The relationship between neural activity and the behaviour of the organism is complex and still poorly understood. There have been attempts to explain this using the notion of synchronization, but the participating neurons are fungible, their activity transient and stochastic, and their dynamics highly variable. In spite of this, the behaviour of the organism may be quite robust. The phenomenon of transient induced global response synchronization (TIGoRS) has been used to explain the emergence of stable responses at the global level in spite of marked variability at the local level. TIGoRS is present when an external stimulus to a complex system causes the system’s responses to cluster closely in state space. In some models, a 10% input sample can result in a concordance of outputs of more than 90%. This occurs even though the underlying system dynamics is time varying and inhomogeneous across the system. Previous work has shown that TIGoRS is a ubiquitous phenomenon among complex systems. The ability of complex systems exhibiting TIGoRS to stably parse environmental transients into salient units to which they stably respond led to the notion of Sulis machines which emergently generate a primitive linguistic structure through their dynamics. This paper reviews the notion of TIGoRS and its expression in several complex systems models including driven cellular automata, cocktail party and dispositional cellular automata.


cellular automata, cooperation, emergence, synchronization, TIGoRS


[1] Wilson, E.O. & Holldobler, B., The Ants, Belknap Press: Cambridge, 1990.

[2] Guastello, S.J., Pincus, D. & Gunderson, P.R., Electrodermal arousal between participants in a conversation: nonlinear dynamics for linkage effects. Nonlinear Dynamics Psychology and Life Sciences, 10, pp. 365–399, 2006.

[3] Aertsen, A.D. & Arndt, M., Response synchronization in the visual cortex. Current Opinion in Neurobiology, 3(4), pp. 586–594, 1993.

[4] Eckhorn, R., Bauer, R., Jordan, W., Kruse, W., Munk, M. & Reitboeck, H., Coherent oscillations: a mechanism of feature linking in the visual cortex. Multiple electrode and correlation analyses in the cat. Biological Cybernetics, 60(2), pp. 121–130, 1988.

[5] Mason, A., Nicoll, A. & Stratford, K., Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vivo. The Journal of Neuroscience, 11(1), pp. 72–84, 1991.

[6] Mainen, Z.F. & Sejnowski, T.J., Reliability of spike timing in neocortical neurons. Science, 268(5216), pp. 1503–1506, 1995.

[7] Shadlen, M.N. & Newsome, W.T., Noise, neural codes and cortical organization. Current Opinion in Neurobiology, 4, pp. 569–579, 1994.

[8] Barnes, C.A., Suster, M.S., Shen, J. & McNaughton, B.L., Multistability of cognitive maps in the hippocampus of old rats. Nature, 388, pp. 272–275, 1997.

[9] Jung, M.W. & McNaughton, B.L., Spatial selectivity of unit activity in the hippocampal granular layer. Hippocampus, 3(2), pp. 165–182, 1993.

[10] Quirk, G.J., Mueller, R.U. & Kubie, J.L., The firing of hippocampal place cells in the dark depends on the rat’s recent experience. The Journal of Neuroscience, 10(6), pp. 2008–2017, 1990.

[11] Sulis, W., Collective intelligence: observations and models. In Chaos and Complexity in Psychology, eds. S. Guastello, M. Koopmans & D. Pincus, Cambridge University Press: Cambridge, 2009.

[12] Trofimova, I.N., Sociability, diversity, and compatibility in developing systems: EVS approach. In Formal Descriptions of Developing System, eds. J. Nation, I. Trofimova, J. Rand & W. Sulis, Boston: Kluwer, 2003.

[13] Sulis, W., Naturally occurring computational systems. World Futures, 39(4), pp. 229–241, 1993.

[14] Sulis, W., Tempered neural networks. In Proceedings of the International Joint Conference on Neural Networks ‘92, Vol. III, IEEE Press: Baltimore, 1992.

[15] Sulis, W., Emergent computation in tempered neural networks: dynamical automata. In Proceedings of the World Congress on Neural Networks ‘93 Vol. IV 449, Lawrence Erlbaum: New York, 1993.

[16] Sulis, W., Emergent computation in tempered neural networks: computation theory. In Proceedings of the World Congress on Neural Networks ‘93, Vol. IV 452, Lawrence Erlbaum: New York, 1993.

[17] Sulis, W., Driven cellular automata. In 1993 Lectures in Complex Systems, eds. D. Stein & L. Nadel, Addison-Wesley: New York, 1993.

[18] Sulis, W., Driven cellular automata, adaptation, and the binding problem. In Advances in Artificial Life, Lectures Notes in Artificial Intelligence 929, eds. F. Moran, A. Moreno, J. Merelo & P. Chacon, Springer-Verlag: New York, 1995.

[19] Sulis, W., Synchronization, TIGoRS, and information flow in complex systems. Nonlinear Dynamics Psychology and Life Science April, In Press, 2016.

[20] Sulis, W., Archetypal dynamics, emergent situations and the reality game. Nonlinear Dynamics Psychology and Life Science, 14(3), pp. 209–238, 2010.

[21] Dufort, P.A. & Lumsden, C.J., Dynamics, complexity and computation. In Physical Theory in 

Biology: Foundations and Exploration, eds. C.J. Lumsden, W.A. Brandts & L.E.H. Trainor, World Scientific Press: Singapore, 1997.