1. A method of adaptively altering the activation function of an artificial neuron including the steps of storing in a memory a number of weighting values at least equal to the number of values which are included in an input vector and a wave value corresponding to at least one of the slope and the period of a wave function representing at least one activation function of said artificial neuron, computing at least one periodic wave function from said period and said weighting values, including computing phase and amplitude values of said periodic wave function from said weighting values, deriving at least one value from said periodic wave function corresponding to a value included in an input vector, adding all said values derived from said at least one periodic function, writing at least one input vector to memory, and counting and storing the number of input vectors written to said memory.