Neural Computation Runs on Dissipative Structures Under Cyclic Calibration: A Systems Perspective on Sleep
Abstract
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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BibTeX
@misc{diaz2026neural,
title = {{Neural Computation Runs on Dissipative Structures Under Cyclic Calibration: A Systems Perspective on Sleep}},
author = {Javier Diaz},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21486},
eprint = {2610.21486},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21486},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Neural Computation Runs on Dissipative Structures Under Cyclic Calibration: A Systems Perspective on Sleep
AU - Javier Diaz
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21486
UR - https://arxiv.org/abs/2610.21486
AB - 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.
ER -