Artificial Neural Networks

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.

The data structures and functionality of neural nets are designed to simulate associative memory. Neural nets learn by processing examples, each of which contains a known “input” and “result,” forming probability-weighted asociations between the two, which are stored within the data structure of the net itself. (The “input” here is more accurately called an input set, since it is generally comprised of multiple independent variables, rather than a single value.) Thus, the “learning” of a neural net from a given example is the difference in the state of the net before and after processing the example. After being given a sufficient number of examples, the net becomes capable of predicting results from inputs, using the associations built from the example set. If a feedback loop is provided to the neural net about the accuracy of its predictions, it continues to refine its associations, resulting in an ever-increasing level of accuracy. In short, there is a direct relationship between the number and diversity of examples processed by a neural net and the accuracy of its predictions. This is why a neural net gets “better” with use. What is interesting about neural nets is that because they are indiscriminate in the way they form associations, they can form unexpected associations, and reveal relationships and dependencies that were not previously known.

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