A Feature-Value Network as a Brain Model
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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 neuron synchronization. The paper then suggests that the concepts are neuron-based and firing neurons are concept instances. Therefore, features become the static framework of the interconnected neural system and concepts are combinations of these, as determined by an external stimulus and the neural associations. Along with this statistical model, it is possible to propose a simplified design for the neuron itself, but based on the idea that it can vary its input and output signals. Some test results also help to support the theory.
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