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.