Before we look into what a neural network is, we should say a few words about its structure. The basis of any neural network is two layers: one receives and distributes incoming signals, and the other processes them. It is this type of network (where there are only two layers) that is called single-layer, and a multi-layer network is one in which there are more of these layers. Thus:
In a single-layer neural network, signals, upon reaching the receiving layer, are immediately redirected to the processing layer, where the result is calculated.
In a multilayer ANN, there is an input, hidden and output layer. In the hidden layer, partial processing of the signal received by the distribution layer occurs, and then the information goes to the processing layer of neurons, where the result is formed.
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What is remarkable is that the use of hidden layers has become available relatively recently, and this is a truly serious breakthrough. Because the performance and capabilities of multilayer networks are much higher compared to single-layer ones.
It is also important that networks are capable of performing distribution operations both in the forward and reverse directions. That is, direct distribution networks are excellent at forecasting, recognition, clustering, but signals in them go only in one direction and do not return back.
And in networks capable of feedback, the possibilities are much wider. Here signals can be transmitted from neurons in the opposite direction. In essence, these are ANNs that have access to the functions of short-term memory, working on the principle of human memory.