Emergence of brain function from structure: an algebraic quantum model

Elkaïoum M. Moutuou, Habib Benali
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Abstract

A fundamental paradigm in neuroscience is that cognitive functions -- such as perception, learning, memory, and locomotion -- are governed by the brain's structural organization. Yet, the theoretical principles explaining how the physical architecture of the nervous system shapes its function remain elusive. Here, we combine concepts from quantum statistical mechanics and graph C*-algebras to introduce a theoretical framework where functional states of a structural connectome emerge as thermal equilibrium states of the underlying directed network. These equilibrium states, defined from the Kubo-Martin-Schwinger states formalism (KMS states), quantify the relative contribution of each neuron to the information flow within the connectome. Using the prototypical connectome of the nematode {\em Caenorhabditis elegans}, we provide a comprehensive description of these KMS states, explore their functional implications, and establish the predicted functional network based on the nervous system's anatomical connectivity. Ultimately, we present a model for identifying the potential functional states of a detailed structural connectome and for conceptualizing the structure-function relationship.
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大脑功能从结构中产生:一个代数量子模型
神经科学的一个基本范式是认知功能(如感知、学习、记忆和运动)受大脑结构组织的支配。在这里,我们结合量子统计力学和图 C* 矩阵的概念,引入了一个理论框架,在这个框架中,结构连接体的功能状态是作为底层定向网络的热平衡状态出现的。这些平衡态由库勃-马丁-施文格状态形式主义(Kubo-Martin-Schwinger states formalism,KMS状态)定义,量化了每个神经元对连接组内信息流的相对贡献。我们利用线虫{(Caemenorhabditis elegans}}的原型连接组,全面描述了这些KMS状态,探讨了它们的功能含义,并根据神经系统的解剖连接建立了预测的功能网络。最终,我们提出了一种模式,用于识别详细结构连接组的潜在功能状态,并将结构与功能的关系概念化。
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