Neural networks in the classical sense refer to a population of neurons interconnected by synapses to carry out a specific function when activated. Such networks are interconnected on a large scale to carry out complex activities.
Artificial Intelligence (AI) is inspired by how this system in the brain processes human information. AI consists of a set of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), and uses a mathematical or computational model for information processing. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
In more practical terms neural networks are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
The basic unit of computation in a neural network is the neuron, often called a node or unit. It receives input from some other nodes, or from an external source and computes an output. Each input has an associated weight (w), which is assigned on the basis of its relative importance to other inputs.