Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- 9 Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation
- 10 Applying artificial neural networks to the study of prey colouration
- 11 Artificial neural networks in models of specialisation, guild evolution and sympatric speciation
- 12 Probabilistic design principles for robust multi-modal communication networks
- 13 Movement-based signalling and the physical world: modelling the changing perceptual task for receivers
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
10 - Applying artificial neural networks to the study of prey colouration
from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
Published online by Cambridge University Press: 05 July 2011
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- 9 Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation
- 10 Applying artificial neural networks to the study of prey colouration
- 11 Artificial neural networks in models of specialisation, guild evolution and sympatric speciation
- 12 Probabilistic design principles for robust multi-modal communication networks
- 13 Movement-based signalling and the physical world: modelling the changing perceptual task for receivers
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
Summary
Introduction
In this chapter I will examine the use of artificial neural networks in the study of prey colouration as an adaptation against predation. Prey colouration provides numerous spectacular examples of adaptation (e.g. Cott, 1940; Edmunds, 1974; Ruxton et al., 2004). These include prey colour patterns used to disguise and make their bearers difficult to detect as well as brilliant colourations and patterns that prey may use to deter a predator. As a consequence, prey colouration has been a source of inspiration for biologists since the earliest days of evolutionary biology (e.g. Wallace, 1889).
The anti-predation function of prey colouration is evidently a consequence of natural selection imposed by predation. More specifically, it is the predators' way of processing visual information that determines the best possible appearance of the colouration of a prey for a given anti-predation function and under given conditions. Because predators' ability to process visual information has such a central role in the study of prey colouration, it follows that we need models that enable us to capture the essential features of such information processing.
An artificial neural network can be described as a data processing system consisting of a large number of simple, highly interconnected processing elements (artificial neurons) in an architecture inspired by biological nerve systems (Tsoukalas & Uhrig, 1997). Artificial neural networks provide a technique that has been applied in various disciplines of science and engineering for tasks such as pattern recognition, categorisation and decision making, as well as a modelling tool in neural biology (e.g. Bishop, 1995; Haykin, 1999).
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- Information
- Modelling Perception with Artificial Neural Networks , pp. 215 - 235Publisher: Cambridge University PressPrint publication year: 2010
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