In this work we propose a complex biologically inspired decision-making neural network in order to trigger motor behaviors in a mobile robotic automaton. Such behaviors are fleeing, exploring, and waiting and are chosen after balancing appetitive/desirable stimuli versus aversive/undesirable stimuli present at the input, from the environment in which the automaton is located. In addition, it is sought that the automaton obtains historical learning from the decisions that it has previously made.