Foraging in an uncertain environment using predictive Hebbian learning.

P Read Montague   Peter Dayan   Terry Sejwnoski
In NIPS 6, 598-605.

Abstract

To survive, an animal must use sensory events to predict the presence of mates, food, danger, and various other stimuli. We present a concrete model which uses diffuse neurotransmitter systems to implement a predictive version of a Hebbian rule, embedded in a feasible neural architecture. When we simulated foraging in an uncertain environment, the model captured the strategies seen in the behavior of bees and a number of other animals. The predictive model suggests a unified way in which neuromodulatory influences can be used to bias actions and control synaptic plasticity.

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