标题： 不同的弱非对称相互作用作为概率通量塑造人类大脑功能 显示英文标题

标题： Distinct weak asymmetric interactions shape human brain functions as probability fluxes

摘要： 人类大脑的功能计算源于其底层神经网络的集体行为。新兴技术使得记录神经元群体活动成为可能，神经网络理论有望从数据中解释和提取功能计算。从热力学角度来看，人体大部分能量消耗由大脑完成，而人类大脑的功能计算似乎涉及高能耗。然而，人类大脑在执行功能时并不增加其能量消耗，且大部分能量消耗并未参与特定的大脑功能：人类大脑如何在不显著改变能量消耗的情况下完成其广泛的功能计算？在此，我们提出一种机制，通过微妙修改大脑区域之间的相互作用网络来执行功能计算。我们首先表明，通过分析自发和任务诱发的全皮层活动数据，概率通量——即状态转换的微观不可逆度量——表现出独特的模式，这取决于所进行的任务，表明人类大脑功能是大脑状态转换的独特序列。然后，我们将不对称相互作用的伊辛自旋系统参数进行拟合，我们发现大脑区域之间的对称相互作用是强烈的且与任务无关，但反对称相互作用是微妙的且与任务相关，推断出的模型能够再现大多数观察到的概率通量模式。我们的结果表明，人类大脑通过微妙地修改大脑区域之间的反对称相互作用来执行其功能计算，这可能仅需要少量的能量。 显示英文摘要

摘要： The functional computation of the human brain arises from the collective behaviour of the underlying neural network. The emerging technology enables the recording of population activity in neurons, and the theory of neural networks is expected to explain and extract functional computations from the data. Thermodynamically, a large proportion of the whole-body energy is consumed by the brain, and functional computation of the human brain seems to involve high energy consumption. The human brain, however, does not increase its energy consumption with its function, and most of its energy consumption is not involved in specific brain function: how can the human brain perform its wide repertoire of functional computations without drastically changing its energy consumption? Here, we present a mechanism to perform functional computation by subtle modification of the interaction network among the brain regions. We first show that, by analyzing the data of spontaneous and task-induced whole-cerebral-cortex activity, the probability fluxes, which are the microscopic irreversible measure of state transitions, exhibit unique patterns depending on the task being performed, indicating that the human brain function is a distinct sequence of the brain state transitions. We then fit the parameters of Ising spin systems with asymmetric interactions, where we reveal that the symmetric interactions among the brain regions are strong and task-independent, but the antisymmetric interactions are subtle and task-dependent, and the inferred model reproduces most of the observed probability flux patterns. Our results indicate that the human brain performs its functional computation by subtly modifying the antisymmetric interaction among the brain regions, which might be possible with a small amount of energy.