Term Paper Neural Networks

Term Paper Neural Networks-53
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.

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.

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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.

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.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.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.Such ideas were appealing but very difficult to implement.In addition, von Neumann architecture was gaining in popularity.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 systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images.

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