It is predated by three publications only: the seminal work of McCulloch and Pitts (1943) that hypothesized how neurons might work by relying on analogy to electrical circuits; Donald Hebb's book The Organization of Behavior (1949), which famously stipulated the basic principle of association of neurons by means of neural co-activation (i.e., assembling); and Frank Rosenblatt's work on the Perceptron (Rosenblatt, 1958). Importantly, however, the Widrow-Hoff rule was the first one that was successfully applied to real-life problems (e.g., noise cancellation in telephone lines which is used to date; cf., Haykin, 1999). After the initial excitement and until the (more) recent successes, models such as those mentioned above that were inspired biologically or, more specifically, neurally were ignored in favour of machines implementing von Neumann's traditional architecture. During the 1970s, the Widrow-Hoff rule was accidentally rediscovered in Psychology by Rescorla and Wagner (1972) who worked on animal and human learning, and by Kohonen (1972) in his work on Self-Organizing Maps in Computer Science. Finally, the widely known and successful Connectionist Parallel-Distributed Processing Models have the Widrow-Hoff rule as their principal building block (cf., McClelland & Rumelhart, 1986).