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
- Part IV Methodological issues in the use of simple feedforward networks
- 14 How training and testing histories affect generalisation: a test of simple neural networks
- 15 The need for stochastic replication of ecological neural networks
- 16 Methodological issues in modelling ecological learning with neural networks
- 17 Neural network evolution and artificial life research
- 18 Current velocity shapes the functional connectivity of benthiscapes to stream insect movement
- 19 A model biological neural network: the cephalopod vestibular system
- Index
- References
16 - Methodological issues in modelling ecological learning with neural networks
from Part IV - Methodological issues in the use of simple feedforward networks
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
- Part IV Methodological issues in the use of simple feedforward networks
- 14 How training and testing histories affect generalisation: a test of simple neural networks
- 15 The need for stochastic replication of ecological neural networks
- 16 Methodological issues in modelling ecological learning with neural networks
- 17 Neural network evolution and artificial life research
- 18 Current velocity shapes the functional connectivity of benthiscapes to stream insect movement
- 19 A model biological neural network: the cephalopod vestibular system
- Index
- References
Summary
16.1 Introduction
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.
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- Modelling Perception with Artificial Neural Networks , pp. 318 - 333Publisher: Cambridge University PressPrint publication year: 2010