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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
Summary

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.

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Modelling Perception with Artificial Neural Networks
  • Online ISBN: 9780511779145
  • Book DOI: https://doi.org/10.1017/CBO9780511779145
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