The basic artificial neuron

The building block of a neural network is the abstraction of a biological neuron, a quite simplistic but powerful computational unit that was proposed for the first time by F. Rosenblatt in 1957, to make up the simplest neural architecture, called a perceptron, that we are going to analyze in the next section. Contrary to Hebbian Learning, which is more biologically plausible but has some strong limitations, the artificial neuron has been designed with a pragmatic viewpoint and, of course, only its structure is based on a few elements characterizing a biological cell. However, recent deep learning research activities have unveiled the enormous power of this kind of architecture. Even if there are more complex and specialized computational cells, the basic artificial neuron can be summarized as the conjunction of two blocks, which are clearly shown in the following diagram:

The input of a neuron is a real-valued vector x ∈ ℜⁿ, while the output is a scalar y ∈ ℜ...