Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.

翻译：由雪崩辅助网络计算和计算神经网络与物理、计算机科学(计算理论以及统计或机器学习)和神经科学领域有关。 我们在这里显示,在门槛网络中自动产生复杂的布尔函数的计算,这是连接和对立(震动)的函数,由逻辑自动数据(motifs)以计算级联的形式计算。我们解释了由于motifs的功能概率及其与功能空间的对称关系,这些模型的计算复杂性及其根据功能概率排列的等级顺序之间新出现的反向关系。我们还表明,这里观察到的最佳抑制部分有助于计算神经科学的结果,与最佳信息处理有关。