Neural Networks – History

In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work. In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits.

In 1949, Donald Hebb wrote The Organization of Behavior, a work which pointed out the fact that neural pathways are strengthened each time they are used, a concept fundamentally essential to the ways in which humans learn. If two nerves fire at the same time, he argued, the connection between them is enhanced.

As computers became more advanced in the 1950’s, it was finally possible to simulate a hypothetical neural network. The first step towards this was made by Nathanial Rochester from the IBM research laboratories. Unfortunately for him, the first attempt to do so failed.

In 1959, Bernard Widrow and Marcian Hoff of Stanford developed models called “ADALINE” and “MADALINE.” In a typical display of Stanford’s love for acronymns, the names come from their use of Multiple ADAptive LINear Elements. ADALINE was developed to recognize binary patterns so that if it was reading streaming bits from a phone line, it could predict the next bit. MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. While the system is as ancient as air traffic control systems, like air traffic control systems, it is still in commercial use.

In 1962, Widrow & Hoff developed a learning procedure that examines the value before the weight adjusts it (i.e. 0 or 1) according to the rule: Weight Change = (Pre-Weight line value) * (Error / (Number of Inputs)). It is based on the idea that while one active perceptron may have a big error, one can adjust the weight values to distribute it across the network, or at least to adjacent perceptrons. Applying this rule still results in an error if the line before the weight is 0, although this will eventually correct itself. If the error is conserved so that all of it is distributed to all of the weights than the error is eliminated.

Despite the later success of the neural network, traditional von Neumann architecture took over the computing scene, and neural research was left behind. Ironically, John von Neumann himself suggested the imitation of neural functions by using telegraph relays or vacuum tubes.

In the same time period, a paper was written that suggested there could not be an extension from the single layered neural network to a multiple layered neural network. In addition, many people in the field were using a learning function that was fundamentally flawed because it was not differentiable across the entire line. As a result, research and funding went drastically down.

This was coupled with the fact that the early successes of some neural networks led to an exaggeration of the potential of neural networks, especially considering the practical technology at the time. Promises went unfulfilled, and at times greater philosophical questions led to fear. Writers pondered the effect that the so-called “thinking machines” would have on humans, ideas which are still around today.

The idea of a computer which programs itself is very appealing. If Microsoft’s Windows 2000 could reprogram itself, it might be able to repair the thousands of bugs that the programming staff made. Such ideas were appealing but very difficult to implement. In addition, von Neumann architecture was gaining in popularity. There were a few advances in the field, but for the most part research was few and far between.

In 1972, Kohonen and Anderson developed a similar network independently of one another, which we will discuss more about later. They both used matrix mathematics to describe their ideas but did not realize that what they were doing was creating an array of analog ADALINE circuits. The neurons are supposed to activate a set of outputs instead of just one.

The first multilayered network was developed in 1975, an unsupervised network.

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