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A three-neuron model of information processing during Bayesian foraging

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Author

Summary, in English

A foraging animal is often confronted with uncertainty of resource abundance. A Bayesian model provides the optimal forgaing policy when food occurrence is patchy. The solution of the Bayesian foraging policy requires elaborate calculations and it is unclear to what extent the policy could be implemented in a neural system. Here we suggest a network architecture of three neurones that approximately can perform an optimal Bayesian foraging policy. It remains to be shown how the network could be self-learned e.g. through Hebbian learning, and how close to to the optimal policy it can perform.

Publishing year

2000

Language

English

Pages

265-270

Publication/Series

Artificial Neural Networks In Medicine and Biology (Perspectives In Neural Computing)

Document type

Conference paper

Publisher

Springer

Topic

  • Ecology

Status

Published

Research group

  • Biodiversity and Conservation Science

ISBN/ISSN/Other

  • ISSN: 1431-6854
  • ISBN: 1-85233-289-1