论文标题
神经元作为量子参考帧的层次结构
Neurons as hierarchies of quantum reference frames
论文作者
论文摘要
数十年来，神经元的概念和数学模型落后于经验理解。在这里，我们扩展了以前的工作，以完全独立于规模的量子信息理论工具对生物系统进行建模，以开发突触，树突和轴突过程，神经元和局部神经元网络的均匀，可扩展的表示。在此表示中，量子参考帧的层次结构充当层次主动推导系统。最终的模型可以对突触活动，树突重塑和营养奖励之间的相关性进行特定的预测。我们总结了如何在发育和再生背景下将模型推广到非神经细胞和组织。
Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.
