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
19 - A model biological neural network: the cephalopod vestibular system
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
19.1 Introduction
Artificial neural networks (ANNs), inspired by the processing systems present in simple nervous systems, are now widely used for the extraction of patterns or meaning from complicated or imprecise data sets (Arbib, 2003; Enquist & Ghirlanda, 2005). Although modern ANNs have progressed considerably from the early, basic feedforward models to systems of significant sophistication, some with varying levels of feedback, modulation, adaptation, learning, etc. (Minsky & Papert, 1969; Gurney, 1997; Vogels et al., 2005), they rarely contain the full processing capabilities or adaptive power of real assemblies of nerve cells. Part of the problem in modelling such capabilities is that the detailed mechanisms underlying the operation of biological neural networks are not themselves fully identified or well understood, for there is a dearth of good biological model systems that possess a wide range of processing mechanisms but whose physiological processes and cellular interconnections can be fully investigated and characterised. One of the best biological model systems available is the vertebrate visual system, but even here the full range of cellular connections and interactions have not yet been characterised and hence cannot be developed into equivalent models (van Hemmen et al., 2001; Wassle, 2004).
- Type
- Chapter
- Information
- Modelling Perception with Artificial Neural Networks , pp. 374 - 389Publisher: Cambridge University PressPrint publication year: 2010