This update algorithm initializes the output and activation value of all input neurons with the input vector. Now the progression of the hidden neurons begins. First the activation and output of each of the hidden neurons is initialized with 0 and the new activation will be calculated. The hidden neuron with the highest activation will be identified. Note that the activation of this winner unit has to be > -1. The class which the input pattern belongs to will be propagated to the output neuron and stored as the neurons activation. This update function is sensible only in combination with the DLVQ learning function.