The neural network model is considered as sequential combination of preprocessing component, neural network and output signal interpreter. For instance, preprocessing component is presented as a set of parallel functioning objects, that transforms input from one or several data fields to signals, which then are sending to an input layer of a network. Each neuron is represented as a node, which consists of two consecutive combining modules. First module combined inputs of neuron with the weights to yield a single value to which the activation function is applied. Varying these functions we can get a different types of neurons.
Neural network is defined as a sequence of neuron layers.
First layer called input layer transmits signals from preprocessing units to neurons on hidden layers. The network connection structure is specified by set of parameters of neuron combination functions. Thus, the structure of connections in a network are specified implicitly, through the list of the references, and the topology of a network is described locally. The various combination functions can have various quantities of parameters and various structure of their description. The name of transformation and list of parameters describe the neuron activation function.