Figure 1.

Structure of motor action learning and reproduction system. The current action feature vector u^(k) is reduced to internal action state v^(k). From recent history w^(k) of the internal action state (bright red is recently activated neuron), we can evaluate the most probable current action sequence state x^(k). An action sequence state stores the probability of an action state within a sequence [w^(k + 1) bright blue node is a more feasible action state]. With this top-down probability and the immediate probability of the next action state (P_t), we can find the posterior probabilities of the incoming action state, v^(k + 1). From the first SOFM layer, this action state maps to action feature vector u^(k + 1). The robot generates the action represented by u^(k + 1).