::Notice !!! All calculations are based on assumption that vectors are normalised.::
The simplest neural network consists of one layer of neurons, which inputs are connected to each other,
Fig. 1. Such network might recognise as much different input vectors (e.g. characters) as the number
of common neural cells is. It is possible when all neurons have distinct synaptic weights.
Fig. 1. Layer of neurons
K - number of neurons,
N - number of neuron inputs.
When the input signal appears X = [x1; ...; xn; ...; xN], every neuron
generates response y(1), ..., y(k), y(K).
Thus, each output signal
has to be compared with others. The simplest method of doing such comparison consists in usage of separate
threshold functions (Fig. 2.) but these threshold values have to be set properly.
Fig. 2. Neuron with threshold function