Artificial neural networks and the study of evolution of prey coloration

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.