The Artificial Neuron
Neural Network Header
Comparison between conventional computers and neural networks
Parallel processing
One of the major advantages of the neural network is its ability to do many things at once. With traditional computers, processing is sequential--one task, then the next, then the next, and so on. The idea of threading makes it appear to the human user that many things are happening at one time. For instance, the Netscape throbber is shooting meteors at the same time that the page is loading. However, this is only an appearance; processes are not actually happening simultaneously.

The artificial neural network is an inherently multiprocessor-friendly architecture. Without much modification, it goes beyond one or even two processors of the von Neumann architecture. The artificial neural network is designed from the onset to be parallel. Humans can listen to music at the same time they do their homework--at least, that's what we try to convince our parents in high school. With a massively parallel architecture, the neural network can accomplish a lot in less time. The tradeoff is that processors have to be specifically designed for the neural network.

The ways in which they function
Another fundamental difference between traditional computers and artificial neural networks is the way in which they function. While computers function logically with a set of rules and calculations, artificial neural networks can function via images, pictures, and concepts.

Based upon the way they function, traditional computers have to learn by rules, while artificial neural networks learn by example, by doing something and then learning from it. Because of these fundamental differences, the applications to which we can tailor them are extremely different. We will explore some of the applications later in the presentation.

The "connections" or concepts learned by each type of architecture is different as well. The von Neumann computers are programmable by higher level languages like C or Java and then translating that down to the machine's assembly language. Because of their style of learning, artificial neural networks can, in essence, "program themselves." While the conventional computers must learn only by doing different sequences or steps in an algorithm, neural networks are continuously adaptable by truly altering their own programming. It could be said that conventional computers are limited by their parts, while neural networks can work to become more than the sum of their parts.

The speed of each computer is dependant upon different aspects of the processor. Von Neumann machines requires either big processors or the tedious, error-prone idea of parallel processors, while neural networks requires the use of multiple chips customly built for the application.

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