A neurobiologically motivated model for self-organized learning

We present a neurobiologically motivated model for an agent which generates a representation of its spacial environment by an active exploration. Our main objectives is the introduction of an action-selection mechanism based on the principle of self-reinforcement learning. We introduce the action-selection mechanism under the constraint that the agent receives only information an animal could receive too. Hence, we have to avoid all supervised learning methods which require a teacher. To solve this problem, we define a self-reinforcement signal as qualitative comparison between predicted an perceived stimulus of the agent. The self-reinforcement signal is used to construct internally a self-punishment function and the agent chooses its actions to minimize this function during learning. As a result it turns out that an active action-selection mechanism can improve the performance significantly if the problem to be learned becomes more difficult.