Foraging in an Uncertain Environment Using Predictive Hebbian Learning

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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P. Montague, Peter Dayan, Terrence J. Sejnowski


Survival is enhanced by an ability to predict the availability of food, the likelihood of predators, and the presence of mates. We present a concrete model that uses diffuse neurotransmitter systems to implement a predictive version of a Hebb learning rule embedded in a neural ar(cid:173) chitecture based on anatomical and physiological studies on bees. The model captured the strategies seen in the behavior of bees and a number of other animals when foraging in an uncertain environment. The predictive model suggests a unified way in which neuromodulatory influences can be used to bias actions and control synaptic plasticity.

Successful predictions enhance adaptive behavior by allowing organisms to prepare for fu(cid:173) ture actions, rewards, or punishments. Moreover, it is possible to improve upon behavioral choices if the consequences of executing different actions can be reliably predicted. Al(cid:173) though classical and instrumental conditioning results from the psychological literature [1] demonstrate that the vertebrate brain is capable of reliable prediction, how these predictions are computed in brains is not yet known.

The brains of vertebrates and invertebrates possess small nuclei which project axons throughout large expanses of target tissue and deliver various neurotransmitters such as dopamine, norepinephrine, and acetylcholine [4]. The activity in these systems may report on reinforcing stimuli in the world or may reflect an expectation of future reward [5, 6,7,8].

*Division of Neuroscience, Baylor College of Medicine, Houston, TX 77030