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This paper posits that neural computation is a dissipative process, relying on metastable structures that require continuous recalibration to maintain information processing parameters like gain and threshold. Sleep is proposed as a systemic phase that resets these parameters, counterbalancing the entropic drift and organizational disruption caused by wakeful activity. This thermodynamic perspective reframes the necessity of sleep not as a homeostatic regulation of unknown variables, but as an emergent property of maintaining computational accuracy in a dissipative system.
Sleep isn't just about restoring energy; it's the essential thermodynamic recalibration that prevents neural computation from succumbing to entropic drift and computational decay.
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.