Predictive Mechanisms in Neuronal Processing for the Generation of simple Semantics in Artificial Agents

Predictive mechanisms and inference processes are known as a means to improve the behavior of artificial systems, like networks or robots. Here we show that it is possible to design predictive control mechanisms with provable convergence properties, which a system can learn through network plasticity. Systems of these kind will arrive at behavioral and synaptic homeostasis at the same time thereby leading to stable synaptic weights. Supporting a "constructivist's perspective", evidence can be provided that during such a process very simple semantic properties arise in the network, which are objectively defined by the structure of the agent's environment and not through subjective interference by the designer of the system. It will be argued that the 'thwarting of predicitons' can possibly be used as one fundamental process to arrive at early cognitive properties in real and artificial systems.