Abstract
In 1949 Donald Olding Hebb formulated a hypothesis describing how neurons excite each other and how the efficiency of this excitation subsequently changes with time. In this paper we present a review of this idea.
We evaluate its influences on the development of artificial neural networks and the way we describe biological neural networks. We explain how Hebb's hypothesis fits into the research both of that time and of present.
We highlight how it has gone on to inspire many researchers working on artificial neural networks. The underlying biological principles that corroborate this hypothesis, that were discovered much later, are also discussed in addition to recent results in the field and further possible directions of synaptic learning research.