Analysing how information flows along the layers of a multilayer perceptron is a topic of paramount importance in the field of artificial neural networks. After framing the problem from the point of view of information theory, in this position article a specific investigation is conducted on the way information is processed, with particular reference to the requirements imposed by supervised learning. To this end, the concept of information matrix is devised and then used as formal framework for understanding the aetiology of optimisation strategies and for studying the information flow. The underlying research for this article has also produced several key outcomes: i) the definition of a parametric optimisation strategy, ii) the finding that the optimisation strategy proposed in the information bottleneck framework shares strong similarities with the one derived from the information matrix, and iii) the insight that a multilayer perceptron serves as a kind of "adaptor", meant to process the input according to the given objective.

翻译：分析信息如何沿多层感知机的各层流动，是人工神经网络领域中一个至关重要的话题。本文首先从信息论的角度对问题进行界定，随后针对信息处理方式展开具体研究，特别关注监督学习所施加的要求。为此，我们提出了信息矩阵的概念，并将其作为理解优化策略成因和研究信息流的形式化框架。本文的基础研究还取得了若干关键成果：i) 提出了一种参数化优化策略的定义；ii) 发现信息瓶颈框架中提出的优化策略与从信息矩阵推导出的策略具有高度相似性；iii) 揭示了多层感知机作为一种“适配器”的本质，其作用是根据给定目标对输入信息进行处理。