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Introduction to Neural NetworksHistory and Development of Neural NetworksAlthough often considered a relatively new phenomenon initial attempts at artificial neural networks date back to the early 20th century. In 1943 Warren McCulloch and Walter Pitts co-authored a paper outlining a model of a simple neural network with electronic circuits. Later, as computers emerged in the 1950s, several researchers attempted to utilise the new technology to create better neural networks. Over the next decade or so these physiologists, psychologists and computer engineers contributed greatly to the development of artificial neural networks. One of these early pioneers was Frank Rosenblatt, a neurobiologist at Cornell that was researching vision in flies. The neural processing that occurred within the eye itself particularly intrigued Rosenblatt and formed the basis of his "Perceptron" neural network. The Perceptron and other models showed great promise with many initial successes. Unfortunately, the early successes of artificial neural networks caused a great deal of hype within the media. This eventually led to disappointment as earlier claims were left unfulfilled. In part this was due to the very limited computing power available at the time. However, there were also conceptual limitations to progress. In 1969 Marvin Minsky and Seymour Papert published a book in which they discussed some of the limitations of the Perceptron model. The effect of these problems was to limit much of the funding available for research into artificial neural networks. Although funding was minimal several scientists continued to develop neural network models. Paul Werbos worked to improve the earlier Perceptron model and created the now popular back-propagation network (though, it wasn't until it was rediscoved in 1986 by Rumelhart and McClelland that it became widely used). Other researchers such as Steve Grossberg, Teuvo Kohonen, and Henry Klopf also created new models. However, it was not until John Hopfield of Caltech presented a paper to the national Academy of Sciences that general interest in the field began to resurge. Hopfield's approach was not simply to create models but to develop technologies that could be applied to real life problems. Several books and conferences followed and provided a forum for people within the field to discuss the topic. In 1985 the American Institute of Physics hosted the first annual meeting on Neural Networks for Computing and by 1987 the Institute of Electrical and Electronic Engineers (IEEE) first International Conference on Neural Networks drew more than a thousand attendees. This interest has more or less continued through to the present day and artificial neural networks have now found uses in everything from medical diagnosis equipment to speech recognition software. Index | Introduction | History and Development References: W. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7:115 - 133, 1943 F. Rosenblatt. Principles of Neurodynamics. Spartan Books, 1962 M. Minsky and S. Papert. Perceptrons. MIT Press, 1969 P. Werbos. Beyond regression: New tools for prediction and analysis in the behavioural sciences. PhD thesis, Harvard University, Cambridge, MA., 1974 D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning representations by back-propagating errors. Nature, 323:533 - 536, 1986 J.J. Hopfield. Neural networks and physical systems with emergent collective computational properties. Proceedings of the National Academy of Sciences of the USA, 79:2554 - 2588, 1982. © 2008 Marcus bros
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