23 Rue des Lavandières, 11160 Caunes Minervois, France.
Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, IL 61920, USA; Adjunct Faculty, Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Biosystems. 2022 Sep;219:104714. doi: 10.1016/j.biosystems.2022.104714. Epub 2022 Jun 6.
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.
几十年来，神经元的概念和数学模型一直落后于经验理解。在这里，我们扩展了以前使用完全与比例无关的量子信息理论工具对生物系统进行建模的工作，以开发突触、树突和轴突过程、神经元以及神经元局部网络的统一、可扩展的表示。在这种表示中，量子参考系层次结构充当分层主动推理系统。由此产生的模型能够对突触活动、树突重塑和营养奖励之间的相关性进行具体预测。我们总结了如何将该模型推广到发育和再生背景下的非神经细胞和组织。
