A spiking neural networks neurons can viewedas feature detectors or alternatively instances of hieroglyphic symbols defined by the associated features they represent .The set of activations at any time step then represent a document written in this alphabet. If we feed this information from the previous time step back to the spiking neural network at each time step ,the network will navigate its own space of internal representations and form a grounded language in which to analyze its own internal states and to guide their evolution. We describe this method and how it could be used by the algorithm to plan and design connections and critic its own thought processes if all of this increases the expected reward. We also show a simple method for an agent to learn levels of abstractions ordered by priority that ultimately increase the global expected reward. Each level is associated with a separate scalar output of the neural network at each time step t which is fed back to the agent as part of the state at time t+1. The agent then correlates them with features of the state initially randomly. It however learns the correct assignment by doing it in such a way that it increases the global reward.We describe an equation meant to order these scalar values and the global reward in order of priority and hence induce a heir-achy of needs for the agent. This then forms the basis of goal formation for it