A key attribute of all but the simplest organisms is an ability to modify their actions in the light of experience – that is to learn. This attribute allows individuals to adapt to rapidly changing environments. Learning is a fundamental aspect of animal behaviour (Barnard, 2003). One aspect of animal behaviour where learning has been particularly extensively studied is food gathering (see recent reviews by Adams-Hunt & Jacobs, 2007; Sherry & Mitchell, 2007; Stephens, 2007), and it is this aspect that we will focus on. We use the term ecological learning to describe an organism learning about its environment.
Neural network models are being used increasingly as effective tools for the description and study of animal behaviour (see Enquist & Ghirlanda, 2005 for a review). There are many different techniques that can be used to model animal learning, with Bayesian approaches being one such example. However, with the desire of taking advantage of neural networks' ability to generalise, neural networks have also been used to model stimulus learning in animals, and have even been used to examine the difference between neural network predators that evolve or learn (for example, see Kamo et al., 2002). In this paper we focus solely on the use of neural networks to represent ecological learning (such as a predator learning and generalising over prey) and argue that there are fundamental differences between the way neural network models are generally trained and the way organisms learn.