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Artificial Neural Networks

1. Neural Networks (NNs) are networks of neurons, for example, as found in real (i.e. biological) brains.

2. Artificial Neurons are crude approximations of the neurons found in

brains. They may be physical devices, or purely mathematical constructs.

3. Artificial Neural Networks (ANNs) are networks of Artificial Neurons,

and hence constitute crude approximations to parts of real brains. They

may be physical devices, or simulated on conventional computers.

4. From a practical point of view, an ANN is just a parallel computational

system consisting of many simple processing elements connected together

in a specific way in order to perform a particular task.

5. One should never lose sight of how crude the approximations are, and

how over-simplified our ANNs are compared to real brains.

4. What are Artificial Neural Networks used for?

As with the field of AI in general, there are two basic goals for neural network research:

Brain modeling : The scientific goal of building models of how real brains work. This can potentially help us understand the nature of human intelligence, formulate better teaching strategies, or better remedial actions for brain damaged patients.

Artificial System Building : The engineering goal of building efficient systems for real world applications. This may make machines more powerful, relieve humans of tedious tasks, and may even improve upon human performance. These should not be thought of as competing goals. We often use exactly the same networks and techniques for both. Frequently progress is made when the two approaches are allowed to feed into each other. There are fundamental differences though, e.g. the need for biological plausibility in brain modeling, and the need for computational efficiency in artificial system building.

5.Why are Artificial Neural Networks worth studying?