The most powerful computing machine for doing classification with low energy consumption is the brain with its known and unknown behaviors and working principles. Partially inspired to the brain, neural networks established a paradigm in which the computational power is increased with a trade-off in terms of accuracy and power consumption. Indeed, in such a paradigm, neurons, synapses and the architectures deriving from their combination are used for solving non-linear classification tasks with the objective to reach the highest possible accuracy without any regard to power consumption. Neuromorphic computing aim is to solve the energy-accuracy trade-off by using all the possible knowledge from biology in order to define a scientific paradigm in which the biology knowledge is not only mathematically represented, but also implemented on-chip for taking advantage from Silicon technology higher efficiency in terms of electrical behavior.