A Concept-Value Network as a Brain Model

This paper suggests a statistical framework for describing the relations between the physical and conceptual entities of a brain-like model. In particular, features and concept instances are put into context. This may help with understanding or implementing a similar model. The paper suggests that features are in fact the wiring. With this idea, the actual length of the connection is important, because it is related to firing rates and neuron synchronization. The paper then suggests that concepts are neuron groups that link features and concept instances are the signals from those groups. Therefore, features become the static framework of the interconnected neural system and concepts are combinations of these, as determined by the external stimulus and the neural synaptic strengths. Along with this statistical model, it is possible to propose a simplified design for a neuron, based on an action potential and variable output signal. A strong comparison with Hebbian theory is then proposed, with some test results to support the theory.