Bee foraging in uncertain environments using predictive hebbian learning.
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 stimuli. 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 proposed, this model is based on the neural substrate and was tested in a simulation of bee flight.
D000818:Animals; D001516:Bees / Q000502:physiology*; D003198:Computer Simulation; D004777:Environment; D005247:Feeding Behavior / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology