First, let’s understand what a network is. A network is a system of nodes (connection points) and connections between nodes. Another way to look at it is a bunch of simple components that come together to create a complex system.
Typically, networks are made with the purpose of transmitting and receiving information. Nodes are capable of taking inputs and processing them to produce an output. Connections are responsible for information flow between nodes, which could be unidirectional (one direction only) or bidirectional (information can flow back and forth).
The cool part about networks is that the global behavior of the overall network is emergent. That is, the network has more powerful abilities than its individual components, despite being composed of those components. Each individual component and interaction compounds to create a larger and more effective entity.
An artificial neural network is essentially a computational network based on biological neural networks. These models aim to duplicate the complex network of neurons in our brains (because imagine what a computer can do if it operates like our brains).
So this time, the nodes are programmed to behave like actual neurons. Although they’re really artificial neurons that try to behave like real ones, hence the name “artificial neural network.”