Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.

通过神经元网络的神经元传播激活是大脑中的重要过程。神经处理的另一个关键部分涉及该网络特性随时间变化的适应性，例如连接强度或兴奋性阈值。这种适应可能很慢，例如从多种经验中学习，也可能很快，例如在记忆形成中。这些自适应网络特征可以被认为是激活神经元的信息标准。然后，这被视为紧急信息形成的一种形式。通过称为标准因果关系的过程，通过此类信息确定神经元的激活。在当前的论文中，探讨了因果关系与一般世界动态的时间因式分解原理之间的关系。时间因式分解描述了世界如何在其当前状态下表示有关其过去的信息，进而决定了世界的未来。在本文中，显示了如何更详细地分析这些过程并通过（自适应）网络模型进行建模。