A Concept-Value Network as a Brain Model
This work addresses the problem of understanding brain organization for researchers in neuroscience and AI, but it appears incremental as it builds on existing ideas without introducing a new paradigm.
The paper proposes a statistical framework to model the relationships between physical and conceptual entities in a brain-like system, suggesting that features form a static horizontal framework while concepts are vertically interconnected combinations of these, with implications for neural binding and distributed representation.
This paper suggests a statistical framework for describing the relations between the physical and conceptual entities of a brain-like model. Features and concept instances are put into context, where the paper suggests that features may be the electrical wiring, although chemical connections are also possible. With this idea, the actual length of the connection is important, because it is related to firing rates and neuron synchronization, but the signal type is less important. The paper then suggests that concepts are neuron groups that link feature sets and concept instances are determined by chemical signals from those groups. Therefore, features become the static horizontal framework of the neural system and concepts are vertically interconnected combinations of these. With regards to functionality, the neuron is then considered to be functional and the more horizontal memory structures can even be glial. This would also suggest that features can be distributed entities and not concentrated to a single area. Another aspect could be signal 'breaks' that compartmentalise a pattern and may help with neural binding.