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This paper frames neural computation as a dissipative system that relies on metastable structures undergoing continuous cycles of synthesis, function, and degradation, consuming energy and exporting entropy. It posits that sleep is a crucial systemic recalibration phase that resets signaling parameters (gain, threshold, integration time) which drift during wakeful dissipative processing, thereby maintaining computational accuracy. This perspective reframes sleep need not as homeostatic regulation of unknown variables, but as an emergent property of neural computation under thermodynamic constraints.
Sleep isn't just about rest; it's the essential thermodynamic recalibration that prevents neural computation from degrading its own substrate during wakefulness.
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.