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 be glial. This would also suggest that features can be distributed entities and not concentrated to a single area.

翻译：本文提出了一种统计框架，用于描述类脑模型中物理实体与概念实体之间的关系。特征与概念实例被置于特定语境中，文中提出特征可能对应电学连接（尽管化学连接同样可能）。基于这一观点，连接的实际长度至关重要，因其与放电频率及神经元同步性相关，而信号类型的重要性相对较低。本文进一步提出：概念是连接特征集的神经元群组，概念实例则由这些群组发出的化学信号所决定。因此，特征构成神经系统的静态水平框架，而概念则是这些特征垂直互连的组合体。在功能层面，神经元被视为功能单元，而更趋水平化的记忆结构可能由胶质细胞承担。这也意味着特征可以是分布式实体，而非集中于单一脑区。