Neuroscientists draw lines of separation among structures and functions that they judge different, arbitrarily excluding or including issues in our description, to achieve positive demarcations that permits a pragmatic treatment of the nervous activity based on regularity and uniformity. However, uncertainty due to disconnectedness, lack of information and absence of objects’ sharp boundaries is a troubling issue that prevents these scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial neural networks. Starting from the detection of metamorphoses of shapes inside a Euclidean manifold, we propose a technique to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, that allows the detection of topological holes development and disappearance, makes it possible to solve the problem of uncertainty in the assessment of countless dynamical phenomena, such as cognitive processes, protein homeostasis deterioration, fire propagation, wireless sensor networks, migration flows, and cosmic bodies analysis.

中文翻译：

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消除神经网络中的不确定性。

神经科学家在他们判断不同的结构和功能之间划清界限，任意排除或包括我们描述中的问题，以实现积极的区分，从而允许根据规律性和均匀性对神经活动进行务实的治疗。但是，由于不连贯性，信息不足和对象边界不清晰而导致的不确定性是一个令人困扰的问题，使这些科学家无法在对自然和人工神经网络进行实验评估时选择所需的适当集合/子集。从检测欧氏流形内部形状的变形开始，我们提出了一种检测在它们的交互作用和形状变形过程中发生的拓扑变化的技术。这种方法