PURPOSE: To accelerate the learning of an input layer, and to decelerate the learning of an output layer by correction added to a neuron state by proportionating change to a component and a constant by steps for multiplying the component by a constant decided according to each layer, and decreasing the constant according to the number of the layers from an input layer to an output layer.

CONSTITUTION: In a learning method including each layer of the update of a synapse coefficient based on change Xj,L, a gradient component gj,L is multiplied by a parameter θj,L for deciding the next change of a neuron state. Thus, change Xj,L proportional to -θj,L gj,L' is calculated. Here, θj,L is decided according to the state of a neuron (j) of a layer 1, and when 0≤θ1+≤1, and -gj,L has different codes, θj,L=1, and when -gj,L and Xj,L have the same codes, θj,L=θL+. Thus, the learning of the input layer can be accelerated, and the learning of the output layer can be decelerated by the correction added to the neuron state.