Artificial Neural Networks

3. What are 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.
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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.