In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural systems in the basic kinematic case of clusters of neuron-like units. Here we extend this approach to networks and begin developing a dynamical theory for them. Our approach provides a novel mathematical foundation for neural dynamics and computation which abstracts away from lower-level biophysical details in favor of information-processing features of neural activity. The theory makes predictions concerning such pathologies as schizophrenia, dementias, and epilepsy, for which some evidence has accrued. It also suggests a model of memory retrieval mechanisms. As further proof of principle, we analyze certain energy-like eigenstates of the 13 three-neuron motif classes according to our theory and argue that their quantum-like superpositional nature has a bearing on their observed structural integrity.

中文翻译：

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没有量子物理学的类量子行为 II。神经网络动力学的类量子模型

在早期的工作中，我们为在类神经元单元集群的基本运动学情况下解释神经系统的类量子行为奠定了基础。在这里，我们将这种方法扩展到网络，并开始为它们开发动态理论。我们的方法为神经动力学和计算提供了一个新的数学基础，它从较低级别的生物物理细节中抽象出来，有利于神经活动的信息处理特征。该理论对精神分裂症、痴呆和癫痫等病理做出了预测，对此已有一些证据。它还提出了一种记忆检索机制模型。作为进一步的原理证明，