神经计算在循环校准下的耗散结构上运行：睡眠的系统视角
Neural Computation Runs on Dissipative Structures Under Cyclic Calibration: A Systems Perspective on Sleep
AI总结：
该研究从系统视角提出，睡眠是神经计算在热力学约束下的涌现属性，通过校准功能-结构耦合抵消清醒时的参数漂移，维持计算精度。
摘要：
生物功能在远离热力学平衡的状态下运行，由亚稳态结构维持，这些结构会持续经历合成、功能执行和降解的循环过程——在此过程中消耗能量并排出熵。神经计算是这种耗散组织的典型代表，依赖于参与动态过程的低熵结构，这些动态过程定义了关键的信息传递与处理参数，如增益、阈值和整合时间。本视角提出，睡眠是一种系统校准阶段，用于重置在耗散处理过程中漂移的关键信号参数。在清醒状态下，神经活动会逐渐消耗并破坏其产生的基质结构，导致参数漂移和计算精度降低。睡眠通过重新校准功能-结构耦合来维持多个活动周期的计算精度——即抵消熵增漂移。该视角未通过未知生理变量的稳态调节来解释实验观察到的睡眠需求，而是将其重新定义为热力学约束下神经计算的涌现属性。
英文摘要：
Biological function operates far from thermodynamic equilibrium, sustained by metastable structures that continuously cycle through synthesis, function execution, and degradation--consuming energy and exporting entropy in the process. Neural computation exemplifies this dissipative organization, relying on low-entropy structures engaged in dynamic processes that define key information transfer and processing parameters such as gain, threshold, and integration time. This perspective proposes that sleep serves as a systemic recalibration phase required to reset crucial signaling parameters that drift during dissipative processing. During wakefulness, neural activity progressively consumes and disrupts the organization of the very substrate from which it emerges, leading to parameter drift and reduced computational precision. Sleep maintains computational accuracy across activity cycles by recalibrating function-structure coupling--that is, by counterbalancing entropic drift. Rather than explaining the experimentally observed sleep need through homeostatic regulation of unknown physiological variables, this perspective reframes it as an emergent property of neural computation under thermodynamic constraints.
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