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Bee foraging in uncertain environments using predictive hebbian learning

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Abstract

RECENT work has identified a neuron with widespread projections to odour processing regions of the honeybee brain whose activity represents the reward value of gustatory stimuli1,2. We have constructed a model of bee foraging in uncertain environments based on this type of neuron and a predictive form of hebbian synaptic plasticity. The model uses visual input from a simulated three-dimensional world and accounts for a wide range of experiments on bee learning during foraging, including risk aversion. The predictive model shows how neuromodulatory influences can be used to bias actions and control synaptic plasticity in a way that goes beyond standard correlational mechanisms. Although several behavioural models of conditioning in bees have been proposed3–7, this model is based on the neural substrate and was tested in a simulation of bee flight.

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Authors and Affiliations

  1. Division of Neuroscience, Baylor College of Medicine, Houston, Texas, 77030, USA

    P. Read Montague & Christophe Person

  2. CBCL, Department of Brain and Cognitive Sciences, MIT, E25-201, Cambridge, Massachusetts, 02139, USA

    Peter Dayan

  3. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California, 92037

    Terrence J. Sejnowski

  4. Department of Biology, University of California, San Diego, La Jolla, California, 92093, USA

    Terrence J. Sejnowski

Authors
  1. P. Read Montague
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  2. Peter Dayan
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  3. Christophe Person
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  4. Terrence J. Sejnowski
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Montague, P., Dayan, P., Person, C. et al. Bee foraging in uncertain environments using predictive hebbian learning. Nature 377, 725–728 (1995). https://doi.org/10.1038/377725a0

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