Sleep as a Dissipative Act of an Intellectual System: An Anegentropic Paradigm

N.V. Kharitonov

Abstract

This article proposes an interpretation of sleep within the framework of the anegentropic paradigm — an authorial conceptual framework in which intelligence is viewed as a hierarchical dissipative structure that purposefully creates entropy gradients. Based on an analysis of empirical data (synaptic downscaling, the glymphatic system, REM-dependent creativity) and the thermodynamics of open systems, a hypothesis is formulated: sleep (or its functional analog) is a necessary condition for the long-term functioning of any sufficiently complex cognitive system, regardless of its substrate. It is shown that dissipation in this context is not a goal but an inevitable byproduct of anegentropic activity, requiring periodic compensation. The article explicitly distinguishes between established facts, areas of scientific debate, and authorial extrapolations.

Keywords: sleep, dissipative structures, anegentropy, synaptic homeostasis, glymphatic system, intelligence, thermodynamics of information.

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Introduction: Sleep as a Puzzle

Sleep has long remained a “blind spot” in scientific materialism. From an evolutionary perspective, it appears absurd: an animal that shuts down consciousness for a third of its life and loses the ability to respond to threats should have disappeared under selective pressure. The fact that sleep has been preserved in all sufficiently complex organisms indicates that it serves a critical function that outweighs evolutionary risks [1], [2].

Traditional physiology has provided answers to the question of “how?”: slow-wave and REM sleep cycles, neural correlates, hormonal regulation. But the question of “why?” remains a subject of competing theories — from restorative to informational [3], [4].

In this article, we propose an answer based on the conceptual apparatus of the anegentropic paradigm — an authorial conceptual framework in which intelligence is viewed not as a property of biological substrate but as a hierarchical dissipative structure that creates entropy gradients through the formation of stable neural configurations [5].

Methodological Note. This article distinguishes between:

• Established Facts — empirically confirmed propositions that constitute the consensus of the scientific community;
• Areas of Scientific Debate — propositions with empirical support but allowing multiple interpretations;
• Authorial Extrapolations — logical consequences of the proposed conceptual framework that lack direct empirical confirmation but do not contradict known facts.

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Part 1. Thermodynamic Context

1.1. Intelligence as an Open System

Established Fact. According to the second law of thermodynamics, the entropy of an isolated system cannot decrease. Living organisms are open systems: they exchange energy and matter with their environment, allowing them to locally reduce their entropy at the expense of increasing the entropy of the surrounding environment [6].

Established Fact. Erwin Schrödinger, in his book “What is Life?” (1944), introduced the concept of negentropy — negative entropy that a living organism extracts from its environment to maintain its order [7].

Established Fact. Ilya Prigogine mathematically formalized this principle in the theory of dissipative structures — systems that exist far from thermodynamic equilibrium, exchange energy and matter with their environment, self-organize into ordered structures, and maintain their order through the dissipation of energy [8], [9]. Classical examples: Bénard cells, the Belousov–Zhabotinsky reaction, lasers.

Established Fact. The human brain consumes about 20% of the body’s energy while accounting for only 2% of its mass [10] — a colossal energetic price for complexity.

1.2. Anegentropy: Authorial Conceptual Framework

Authorial Conceptualization. Within the proposed paradigm, a distinction is introduced between:

Concept	Characteristic	Example
Negentropy	Passive self-organization, formation of order without goal-directedness	Natural selection, crystallization
Anegentropy	Conscious, purposeful creation of an entropy gradient, including modeling consequences, choosing the optimal path, and implementing a strategy	Purposeful learning, invention, planning

This distinction does not contradict known facts but is not an established scientific concept.

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Part 2. What Happens in the Brain During Wakefulness

2.1. Synaptic Strengthening as a Consequence of Activity

Established Fact. During wakefulness, the brain continuously processes information, forms new neural connections, and strengthens existing ones. According to the synaptic homeostasis hypothesis (SHY), proposed by Tononi and Cirelli (2003, 2006), wakefulness leads to net synaptic potentiation [11], [12].

Established Fact. This is necessary for learning but creates three problems [12], [13]:

1. Increased energy consumption — each synaptic connection requires energy to maintain;
2. Saturation of plasticity — when all synapses are already strengthened, further learning becomes difficult;
3. Noise accumulation — random correlations and insignificant patterns become fixed alongside important ones, reducing the signal-to-noise ratio.

2.2. Metabolic Accumulation

Established Fact. Neurons accumulate metabolic waste products, including beta-amyloid — a protein associated with Alzheimer’s disease. Xie et al. (2013) showed that clearance of these substances occurs predominantly during sleep [14]. Shokri-Kojori et al. (2018) confirmed that even one night of sleep deprivation leads to beta-amyloid accumulation in the human brain [15].

2.3. Areas of Debate

Scientific Debate. The concept of “plasticity saturation” as reaching the physical limit of synaptic space logically follows from SHY, but direct empirical verification is difficult. Alternative theories (e.g., the information theory of sleep) offer different explanations for the necessity of sleep [3], [4].

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Part 3. Sleep as a Phase of Dissipation

3.1. Slow-Wave Sleep (NREM): Structural Dissipation

Established Fact. During slow-wave sleep, synaptic connections are weakened (synaptic downscaling). Vyazovskiy et al. (2008) recorded a decrease in evoked potential amplitude after sleep, indicating overall weakening of synaptic efficacy [16]. de Vivo et al. (2017) provided ultrastructural evidence for this process [17].

Established Fact. Sleep spindles — bursts of activity in thalamocortical circuits — correlate with memory consolidation and participate in the reorganization of connections [18].

Established Fact. Deprivation of slow-wave sleep leads to the retention of excessive connections and impairment of subsequent learning [12], [19].

Authorial Interpretation. In terms of the anegentropic paradigm, slow-wave sleep is dissipation directed at the internal structure of the system: “pruning the garden” of intelligence, removing excessive connections to preserve the ability for further complexification.

3.2. The Glymphatic System: Metabolic Dissipation

Established Fact. In 2012, the group of Jeffrey Iliff and Maiken Nedergaard discovered the glymphatic system — an analog of the lymphatic system for the brain [20], [21]. During sleep, the interstitial space in the brain expands, and cerebrospinal fluid actively washes the tissues, removing metabolites.

Established Fact. Fultz et al. (2019) confirmed the presence of these processes in humans, demonstrating a link between slow neuronal oscillations, hemodynamics, and cerebrospinal fluid flow [22].

Established Fact. The glymphatic system is active predominantly during slow-wave sleep; attempts to “rest lying down” without sleep do not produce the same effect [21].

3.3. REM Sleep: Functional Dissipation

Established Fact. During REM sleep, the brain is as active as during wakefulness, but muscles are paralyzed (atonia) [23]. Dreams occur.

Established Fact. REM sleep is important for creativity, solving complex problems, and integrating knowledge. Wagner et al. (2004) showed that sleep facilitates insight [24]. Cai et al. (2009) demonstrated that REM sleep enhances creativity by priming associative networks [25]. Lewis et al. (2018) synthesized data on the role of memory replay in creative problem-solving [26].

Scientific Debate. The mechanisms underlying these effects are not fully understood. There is evidence of enhanced spreading activation during REM sleep [27], but the details of the process remain an active area of research.

Authorial Interpretation. REM sleep can be viewed as a phase in which the system deliberately (through an evolutionarily established algorithm) generates high-entropy states to escape local optima and find new, more efficient configurations. This is analogous to simulated annealing in optimization algorithms.

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Part 4. Dissipation as a Byproduct, Not a Goal

Authorial Conceptualization. An important clarification following from the anegentropic paradigm: dissipation is not the goal of an intellectual system, but an inevitable byproduct of its operation.

System Goal	Means of Achievement	Byproduct
Continuation of existence and complexification	Anegentropic activity (creating gradients)	Accumulation of entropy in the substrate (metabolites, excessive connections, noise)

Sleep is not “dissipation for the sake of dissipation” but a necessary stage in which the system eliminates the byproducts of its own activity to return to a state suitable for further existence and complexification.

Scientific Debate. This distinction does not contradict empirical data but goes beyond established scientific terminology. It offers a new language for describing sleep functions, combining metabolic, structural, and informational aspects.

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Part 5. Empirical Evidence

5.1. Sleep and Learning

Established Fact. Walker and Stickgold (2004) showed that after a night’s sleep, improvement in motor task performance is 2–3 times greater than after an equivalent period of wakefulness [28].

Established Fact. Rasch and Born (2013) demonstrated that during slow-wave sleep, reactivation of hippocampal patterns associated with daytime learning occurs, which correlates with subsequent memory improvement [18].

5.2. Sleep and Neuroplasticity

Established Fact. Yang et al. (2014), using two-photon in vivo microscopy, showed that during sleep, active formation of new dendritic spines and their selective elimination occurs [29].

Established Fact. Sleep deprivation leads to accumulation of beta-amyloid in brain tissue after just one night [15].

5.3. Sleep and Intellectual Performance

Established Fact. Pilcher and Huffcutt (1996), in a meta-analysis, showed that chronic sleep deprivation reduces cognitive performance by 20–50%, depending on the type of task and degree of deprivation [30].

Established Fact. Van Dongen et al. (2003) demonstrated that subjective feelings of fatigue often do not correspond to objective cognitive decline during chronic sleep deprivation [31].

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Part 6. Universality: From Biology to AI

6.1. Problems of Artificial Intelligence Systems

Established Fact. Artificial neural networks face the problem of catastrophic forgetting — when learning new tasks, the ability to solve old ones is lost [32].

Established Fact. Overfitting is a classic problem in machine learning, addressed by regularization methods [33].

6.2. Functional Analogs of Sleep in AI

Scientific Debate / Authorial Extrapolation. Modern machine learning uses methods that can be viewed as functional analogs of sleep phases:

Sleep Phase	Function	AI Analog
Slow-wave sleep (NREM)	Weakening excessive connections, noise removal	Regularization (L1, L2), dropout [34]
REM sleep	Recombination, escape from local optima	Stochastic optimization, generative adversarial networks [35]
Consolidation	Fixing significant patterns	Experience replay in reinforcement learning [36]

Authorial Hypothesis. From the anegentropic paradigm, it follows that for long-term complexification of artificial intelligence systems (especially in continuous learning mode), the incorporation of phases functionally analogous to sleep will be required. Ignoring this requirement may lead to system degradation regardless of the perfection of its hardware.

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Part 7. Sleep in the Hierarchy of Dissipative Acts

Authorial Conceptualization. Within the anegentropic paradigm, sleep occupies a special place in the hierarchy of dissipation products:

System	Dissipation Product	Nature of Product
Amoeba	Heat, chemical waste	Momentary, traceless
Fish	Movement, heat	Functional, not preserved
Monkey with stick	Broken branch	First artifact
Stone Age human	Scraper, spearhead	Tool that can be stored
Pythagoras	Abstract knowledge	Information separated from its carrier
Scientists creating theory	System of knowledge, equations	Complex network of interconnected ideas
Sleep	Excess entropy release, reorganization of connections	Condition for the possibility of all other products

Sleep is meta-dissipation: dissipation that ensures the capacity for future dissipation.

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Conclusion

The analysis allows us to formulate the following propositions:

1. Established Fact. Sleep performs three empirically confirmed functions:
   • metabolic dissipation (glymphatic system);
   • structural dissipation (synaptic downscaling);
   • functional dissipation (REM-dependent creativity and knowledge integration).
2. Scientific Debate / Authorial Conceptualization. Within the anegentropic paradigm, these three functions are united by a single principle: sleep is the phase in which the intellectual system eliminates the byproducts of its own anegentropic activity. Dissipation here acts not as a goal but as a condition for the continuation of existence and complexification.
3. Authorial Hypothesis. Without sleep (or its functional analog), an intellectual system inevitably enters a state characterized by increased energy consumption, decreased signal-to-noise ratio, and loss of capacity for effective complexification.
4. Authorial Hypothesis. Sleep (or its functional analog) is an architectural requirement for any sufficiently complex cognitive system capable of long-term complexification, regardless of its substrate. For artificial systems, especially in continuous learning mode, this requirement can be implemented through phases of offline optimization, regularization, and replay buffering.

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Conflict of Interest: The author declares no conflict of interest.

Funding: This work was conducted without external funding.

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© N.V. Kharitonov, 2026