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  • Print publication year: 2010
  • Online publication date: July 2011

12 - Probabilistic design principles for robust multi-modal communication networks

from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology


12.1 Stochastic multi-modal communication

Biological systems are inherently noisy and typically comprised of distributed, partially autonomous components. These features require that we understand evolutionary traits in terms of probabilistic design principles, rather than traditional deterministic, engineering frameworks. This characterisation is particularly relevant for signalling systems. Signals, whether between cells or individuals, provide essential integrative mechanisms for building complex, collective, structures. These signalling mechanisms need to integrate, or average, information from distributed sources in order to generate reliable responses. Thus there are two primary pressures operating on signals: the need to process information from multiple sources, and the need to ensure that this information is not corrupted or effaced. In this chapter we provide an information-theoretic framework for thinking about the probabilistic logic of animal communication in relation to robust, multi-modal, signals.

There are many types of signals that have evolved to allow for animal communication. These signals can be classified according to five features: modality (the number of sensory systems involved in signal production), channels (the number of channels involved in each modality), components (the number of communicative units within modalities and channels), context (variation in signal meaning due to social or environmental factors) and combinatoriality (whether modalities, channels, components and/or contextual usage can be rearranged to create different meaning). In this paper we focus on multi-channel and multi-modal signals, exploring how the capacity for multi-modality could have arisen and whether it is likely to have been dependent on selection for increased information flow or on selection for signalling system robustness.

Amari, S. 1985. Differential-Geometric Methods in Statistics. Lecture Notes in Statistics 28. Springer-Verlag.
Ay, N. & Krakauer, D. C. 2007. Geometric robustness theory and biological networks. Theory in Biosciences 125(2), 93–121.
Cover, T. M. & Thomas, J. A. 2001. Elements of Information Theory. John Wiley and Sons.
Waal, F. B. M. 1982. Chimpanzee Politics: Power and Sex Among the Apes. Johns Hopkins Press.
Elias, D. O., Mason, A. C., Maddison, W. P. & Hoy, R. R. 2003. Seismic signals in a courting male jumping spider (Araneae: Salticidae). J Exp Biol 206, 4029–4039.
Flack, J. C. & Waal, F. B. M. 2007. Context modulates signal meaning in primate communication. Proc Natl Acad Sci USA 104, 1581–1586.
Grice, H. P. 1969. Utterer's meaning and intention. Phil Rev 68, 147–177.
Hauser, M. D. 1997. The Evolution of Communication. MIT Press.
Hillis, J. M., Ernst, M. O., Banks, M. S. & Landy, M. S. 2002. Combining sensory information: mandatory fusion within, but not between senses. Science 298, 1627–1630.
Johnstone, R. A. 1996. Multiple displays in animal communication: ‘backup signals’ and ‘multiple messages’. Phil Trans R Soc B 351, 329–338.
Krakauer, D. C. & Nowak, M. A. 1999. Evolutionary preservation of redundant duplicated genes. Sem Cell Dev Biol 10, 555–559.
Krakauer, D. C. & Plotkin, J. B. 2004. Principles and parameters of molecular robustness. In Robust Design: a Repertoire for Biology, Ecology and Engineering (ed. Jen, E.). Oxford University Press.
McCulloch, W. S. & Pitts, W. A. 1943. Logical calculus of the ideas immanent in nervous activity. Bull Math Biophyss 5, 115–133.
Nowak, M. A. & Krakauer, D. C. 1999. The evolution of language. Proc Natl Acad Sci USA 96, 8028–8033.
Partan, S. & Marler, P. 1999. Communication goes multimodal. Science 283, 1272–1273.
Pearl, J. 2000. Causality. Cambridge University Press.
Rowe, C. 1999. Receiver psychology and the evolution of multicomponent signals. Anim Behav 58, 921–931.
Tononi, G., Sporns, O. & Edelman, G. M. 1994. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA 91, 5033–5037.
Neumann, J. 1956. Probabilistic logics and the synthesis of reliable organisms from unreliable components. In Automata Studies (ed. Shannon, C. E. & McCarthy), J.. Princeton University Press.
Weiss, D. J. & Hauser, M. D. 2002. Perception of harmonics in the combination long call of cottontop tamarins (Saguinus oedipus). Anim Behav 64, 415–426.