多元相关函数信息的涌现
Emergence of functional information from multivariate correlations
摘要：
符号序列（如核酸或氨基酸序列，也包括神经元放电或字母串）的信息含量可以从这些序列的集合中计算出来，但是因为信息不能被分配给单个序列，我们不能将信息与序列上的其他可观测量相关联。在这里，我们表明，从多变量（多变量）的序列内的“训练”合奏的相关性获得的信息得分可以用来预测可观的样本序列的准确性，规模与相关性的复杂性，显示功能信息出现从多变量相关性的层次结构。
英文摘要：
The information content of symbolic sequences (such as nucleic- or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence. Here we show that an information score obtained from multivariate (multiple-variable) correlations within sequences of a "training" ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
