A Concept-Value Network as a Brain Model

TL;DR

This paper proposes a statistical framework modeling brain-like structures using feature and concept networks, emphasizing physical connections and neuron group interactions for understanding neural representation.

Contribution

It introduces a novel conceptual model linking features and concepts through a brain-inspired network, highlighting the importance of physical wiring and neuron group interactions.

Findings

Features as static horizontal structures

Concepts as neuron groups linking features

Signal 'breaks' may aid neural binding

Abstract

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.