Foraging Through Prediction

To survive, an animal must use sensory events to predict the presence of mates, food, danger, and various other stimuli that are important for its survival and procreation. Although reliable prediction is critical, it is not understood how such prediction is carried out by nervous systems. We present a model which utilizes diffuse neuromodulatory systems to implement a predictive version of a Hebbian rule, and embed this rule in a feasible neural architecture. The predictive model suggests a unified way in which neuromodulatory influences are used to bias actions and control learning. When required to forage in a stochastic environment, the model captures the strategies seen in the behavior of bees and a number of other animals. It further suggests that predictive rules for synaptic plasticity offer a simple framework which is nevertheless more powerful than correlational accounts. Introduction Any animal presented with a real environment must have a means to react adaptively to that ..

A framework for mesencephalic dopamine systems based on predictive Hebbian learning

We develop a theoretical framework that shows how mesen-cephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluc-tuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcottical targets. We present a model for how such errors could be constructed in a real brain that is consistent wit