A three-neuron model of information processing during Bayesian foraging

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

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.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Ecology
Original languageEnglish
Title of host publicationArtificial Neural Networks In Medicine and Biology (Perspectives In Neural Computing)
PublisherSpringer
Pages265-270
ISBN (Print)1-85233-289-1
Publication statusPublished - 2000
Publication categoryResearch
Peer-reviewedNo

Publication series

Name
ISSN (Print)1431-6854