The Hebb Rule for Synaptic Plasticity: Algorithms and Implementations

Publisher Summary The Hebb rule and variations on it have served as the starting point for the study of information storage in simplified neural network models. This chapter presents a framework within which the Hebb rule and other related learning algorithms that serve as an important link between the implementation level of analysis, which is the level at which experimental work on neural mechanisms takes place, and the computational level, on which the behavioral aspects of learning and perception are studied. The chapter illustrates how the Hebb rule can be built out of realistic neural components in several different ways. The notion of an algorithm is central in thinking about information processing in the nervous system. The chapter describes three methods for implementing the Hebb rule, which can be used to form associations between one stimulus and another. Such associations can be either static or they can be temporal.

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