xisting analyses of the Neocognitron failed to discuss the dynamic characteristic during learning because they were all confined to algebraic method. This paper intruduces differentiation to analyse the Neocognitron. For the unsupervised learning, this paper derives a defferential equation to describe us, a condition of us increasing, and an inequality that the initial value of variable weight of a, b must satisfy. For fixed representation or the supervised learning, we obtain an u. function of time. We show that, in this case, learning is a process in which us approximates a coefficience independence of q and the initial value of weight a,b.