A very brief Introduction to Neural Network.


I have huge interests in the mysterious Artificial Intelligence. Because of the lack of time to devote into learning AI, I still barely know AI related theories. Tonight I read something about Neural Network, which plays a important role in the AI field.
A neural network is a massively parallel distributed processor made up of simple processing units that has a natural propensity for storing experiential knowledge and making it available for use. It resemble the brain in two respects:
knowledge is acquired by the network from its environment through a learning process.
Interneuron connection strengths  known as synaptic weights, are used to store the acquired knowledge.
Let us take the human vision as an example to demonstrate why it is necessary to study neural network. Human vision is a very complex information processing task. It is the function of the visual system to provide a representation of the the environment around us and to supply the information we need to interact with the environment. The brain accomplishes recognition task at the same time. For instance, the brain can recognize a familiar face embedded in an unfamiliar scene in shorter than a blink of eye, actually 100-200ms, whereas tasks of much lesser complexity take a great deal longer on a very powerful computer.
What's the reason makes the biological brains so efficient? How to let our machines think and do reasoning like biological brains do. The course of Neural Network has been trying to answer these questions from the day the course was established.
A typical neural network has many useful properties and capacities. 1. Nonlinearity 2. Input-output Mapping 3. Adaptivity 4. Evidential Response 5. Contextual Information 6. Fault Tolerance 7. VLSI(Very Large Scale Integrated) Implementability. 8. Uniformity of Analysis and Design 9. Neurobiological Analogy.