Predictive hebbian learning

Abstract

A creature presented with an uncertain and variable environment needs to anticipate important future events or risk diminished chances for survival. These events can include the presence of food, destructive stimuli, and potential mates. In short, a nervous system must have means to generate guesses about its most likely next state and the most likely next state of the world. Psychologists have studied conditions under which animals can learn to predict future reward and punishment. In this paper, we review the computational theory that may be relevant for understanding this form of learning. Some of the central mechanisms required for predictive learning have been discovered in both vertebrate Ljungberg et al's (1992) and invertebrate brains (Hammer, 1994).