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
15 - The need for stochastic replication of ecological 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
15.1 Introduction
Artificial neural networks are increasingly being used by ecosystem, behavioural and evolutionary ecologists. A particularly popular model is the three-layer, feedforward network, trained with the back-propagation algorithm (e.g. Arak & Enquist, 1993; Ghirlanda & Enquist, 1998; Spitz & Lek, 1999; Manel et al., 1999; Holmgren & Getz, 2000; Kamo et al., 2002, Beauchard et al., 2003). The utility of this design (especially if, as is common, the output layer consists of a single node) is that for a given set of input data, the network can be trained to make decisions, and this decision apparatus can subsequently be applied to inputs that are novel to the network. For example, an ecosystem ecologist with a finite set of ecological, biochemical and bird-occurrence data for a river environment can train a network to produce a predictive tool that will determine the likelihood of bird occurrence through sampling of the environment (Manel et al., 1999). Or in behavioural and evolutionary ecology, a network can be trained to distinguish between a ‘resident animal’ signal and ‘background’ signals, and subsequently used to determine how stimulating a mutant animal signal is, and hence, how signals can evolve to exploit receiver training (Kamo et al., 2002). Reasons for the popularity of the back-propagation training method (Rumelhart et al., 1986) include its computational efficiency, robustness and flexibility with regard to network architecture (Haykin, 1999).
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- Chapter
- Information
- Modelling Perception with Artificial Neural Networks , pp. 308 - 317Publisher: Cambridge University PressPrint publication year: 2010