Bee foraging in uncertain environments using predictive hebbian learning.

@article{citeulike:99681, 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 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.}, address = {Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.}, author = {Montague, P. R. and Dayan, P. and Person, C. and Sejnowski, T. J. }, citeulike-article-id = {99681}, doi = {10.1038/377725a0}, issn = {0028-0836}, journal = {Nature}, keywords = {bee, computationalmodel, foragingtheory, uncertainty, utility}, month = {October}, number = {6551}, pages = {725--728}, posted-at = {2005-02-20 20:02:59}, priority = {0}, title = {Bee foraging in uncertain environments using predictive hebbian learning.}, url = {http://dx.doi.org/10.1038/377725a0}, volume = {377}, year = {1995} }

TY - JOUR ID - citeulike:99681 L3 - citeulike-article-id:99681 TI - Bee foraging in uncertain environments using predictive hebbian learning. JF - Nature VL - 377 IS - 6551 SP - 725 EP - 728 AD - Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA. SN - 0028-0836 N2 - 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. KW - bee KW - computationalmodel KW - foragingtheory KW - uncertainty KW - utility AU - Montague, PR AU - Dayan, P AU - Person, C AU - Sejnowski, TJ PY - 1995/10/26/ UR - http://dx.doi.org/10.1038/377725a0 ER -