The present report examines the coinciding results of two study groups each presenting a power-of-two function to describe network structures underlying perceptual processes in one case and word production during verbal fluency tasks in the other. The former is theorized as neural cliques organized according to the function N = 2i − 1, whereas the latter assumes word conglomerations thinkable as tuples following the function N = 2i. Both theories assume the innate optimization of energy efficiency to cause the specific connectivity structure. The vast resemblance between both formulae motivated the development of a common formulation. This was obtained by using a vector space model, in which the configuration of neural cliques or connected words is represented by a N-dimensional state vector. A further analysis of the model showed that the entire time course of word production could be derived using basically one single minimal transformation-matrix. This again seems in line with the principle of maximum energy efficiency.

中文翻译：

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神经网络函数的向量空间模型：从连通性理论和单词产生的对数时间过程之间的相似性中得到启发

本报告审查了两个研究组的一致结果，每个研究组都提出了一个二次幂函数来描述一个案例中感知过程的网络结构和另一个案例中语言流畅性任务期间的单词生成。前者被理论化为根据函数 N = 2i − 1 组织的神经集团，而后者假设单词组合可被视为遵循函数 N = 2i 的元组。两种理论都假设能量效率的先天优化导致特定的连接结构。两个公式之间的巨大相似性促使开发了一个共同的公式。这是通过使用向量空间模型获得的，其中神经派或连接词的配置由 N 维状态向量表示。对该模型的进一步分析表明，基本上可以使用一个单一的最小变换矩阵来导出单词产生的整个时间过程。这似乎再次符合最大能源效率的原则。