Neural Dissipative Structures: A Non-Quantum Framework for Metastable Cognition
A Proposal for the Thermodynamics of Information in Neural Systems
Author: Luigi Usai
ORCID: https://orcid.org/0009-0003-3001-717X
Location: Quartucciu, Italy
Date: January 1, 2026
Usai, L. (2026). Neural Dissipative Structures A Non-Quantum Framework for Metastable Cognition. Zenodo. https://doi.org/10.5281/zenodo.18118247
Abstract
The Computational Theory of Mind (CTM) faces increasing empirical challenges in explaining the brain’s structural plasticity and thermodynamic efficiency. This paper introduces the Neural Dissipative Structure (NDS) framework, positing that cognition is not a symbolic computation but a sequence of Dissipative Phase Transitions. Grounded in non-equilibrium thermodynamics and Haken’s synergetics, the NDS framework models neural populations as order parameters governed by a Synergetic Fokker-Planck equation. Unlike previous quantum models, the NDS framework operates at the macroscopic scale. Simulation data indicate that cognitive insight corresponds to a bifurcation-induced expansion of informational entropy, followed by a drastic reduction in informational heat dissipation. We propose the “Insight Calorimetry” protocol to detect this specific thermodynamic signature, providing a rigorous path toward falsifying the physical foundations of the mind.
- Introduction: The Thermodynamic Crisis of Cognition
Contemporary neuroscience operates under a fundamental tension: the brain is an open thermodynamic system far from equilibrium, yet its cognitive functions are largely modeled as abstract, energy-independent algorithms. This “Computational Metaphor” fails to explain why neural dynamics exhibit scale-invariant neuronal avalanches (Beggs & Plenz, 2003) and why the brain consumes ~20
Earlier dissipative models, such as the “Dissipative Brain” (Vitiello, 2004), utilized Quantum Field Theory (QFT) to address memory. However, the NDS framework shifts the ontology to macroscopic non-equilibrium thermodynamics. Following Prigogine (1977) and Kelso (2014), we define the brain as a dissipative structure that exploits energy gradients to stabilize metastable attractors. Cognition is thus viewed as the physical process of navigating a self-organized energy landscape through stochastic phase transitions.
- Formal Framework: Synergetic Neural Dynamics
2.1. The Fokker-Planck Order Parameter
We define the Neural Population Vector (
) as a macroscopic order parameter. Its temporal evolution is described by the Synergetic Fokker-Planck equation:
Where:
•
is the Dissipative Potential, conditioned by a control parameter
(e.g., neuromodulatory flux).
•
denotes Stochastic Fluctuations, the thermodynamic engine allowing state-space exploration.
• Cognition emerges during phase transitions—canonically represented by the Hopf Bifurcation—where a change in
slaves individual neuronal degrees of freedom into a coherent, metastable oscillatory state.
2.2. Thermodynamics of Information: The Bifurcation Effect
Simulations of the NDS model reveal that cognitive transitions (Insight) are characterized by an increase in informational entropy (
). This represents an expansion of the system’s state space as it accesses new configuration manifolds. Crucially, as the system settles into this new, broader configuration, the informational heat dissipation (
) drops toward zero. This suggests that insight is a transition from a dissipative exploratory state to a thermally efficient, multi-stable regime.
- Methods: Langevin Dynamics and Landauer Heat Calculation
3.1. System Dynamics
We modeled the neural population vector
using the Langevin equation:
where
is the potential landscape and
rappresenta l’intensità del rumore neurale (0.5).
3.2. Justification of the 1D Model
Although the brain is a high-dimensional system, Haken’s “Slaving Principle” states that near a bifurcation point, the dynamics are dominated by a few order parameters. Therefore, a 1D Langevin model is sufficient to capture the essential thermodynamic features of the phase transition.
3.3. Potential Landscape and Entropy
The bifurcation was modeled by shifting the potential
from a single-well exploratory regime to a multi-stable regime via a control parameter
. Informational entropy
was calculated as
, where
is the instantaneous variance.
3.4. Heat Calculation
Informational heat
was derived using Landauer’s Principle:
, assuming a biological temperature
K.
- Simulation Results: Entropic Expansion and Thermal Quenching
4.1. Pre-Bifurcation Dynamics (
)
The system exhibits a gradual decrease in informational entropy (
) as it settles into a narrow local attractor. This phase is characterized by significant fluctuations in Informational Heat (
), representing the “stochastic cost” of search.
4.2. The Insight Event (Bifurcation at
)
The system undergoes a sharp Entropic Expansion. Entropy (
) jumps from ~0.7 to ~2.2 nats. This indicates that “Insight” is a transition to a more complex configuration manifold.
4.3. Post-Bifurcation Stabilization (
)
Following the bifurcation,
collapses to near-zero values. The new cognitive configuration, despite being more complex, is thermally optimized.
| Feature | Computational (CTM) | Neural Dissipative (NDS) | Quantitative Marker |
| Ontology | Software/Symbolic | Physical Dissipation | Entropy Flux (
) |
| Insight Event | Logical Step | Phase Bifurcation | Entropy Increase (
) |
| Thermodynamics | Constant overhead | Optimized Dissipation | Heat Drop (
) |
| Stability | Rigid Circuits | Metastable/Critical | Power-law (
) |
- Relationship to Existing Models: FEP, Deco, and Rabinovich
NDS complements Friston’s Free Energy Principle (FEP) by providing its physical substrate: while FEP describes the logic of uncertainty minimization, NDS describes the thermodynamic engine of that process. Unlike Deco’s stochastic neurodynamics, which focus on computational efficiency, NDS emphasizes dissipative efficiency. Furthermore, it aligns with Rabinovich’s “transient dynamics” by modeling cognition as a non-linear journey through metastable states. - Discussion: The Metastable Efficiency Paradox
A crucial finding of the NDS framework is the apparent paradox of entropic expansion coupled with thermal quenching. Cognition is the search for higher-entropy manifolds with lower-rate entropy production. By settling into a multi-stable landscape, the brain maximizes its representational capacity while minimizing its physical cost, explaining why biological intelligence is orders of magnitude more efficient than current silicon-based AI. This transition renders the concept of “mental representation” redundant: the attractor isthe meaning. - Limitations and Future Perspectives
The present 1D simulation captures the qualitative phase transition but does not account for the spatial heterogeneity of the cortex. Future work should extend the NDS framework to 2D neural fields to model wave propagation. Furthermore, while the link between Shannon and Clausius entropy is grounded in the Landauer-Parrondo framework, empirical validation via “Insight Calorimetry” is required to confirm the exact transduction coefficients in vivo. - Conclusion
The Neural Dissipative Structure paradigm shifts neuroscience from information theory to non-equilibrium statistical mechanics. The brain is not a computer; it is a macroscopic dissipative structure whose very existence is the process of thought.
References
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- Friston, K. (2010). The free-energy principle: a rough guide to the brain? Nature Reviews Neuroscience.
- Haken, H. (1983). Synergetics: An Introduction. Springer-Verlag.
- Landauer, R. (1961). Irreversibility and Heat Generation in the Computing Process. IBM Journal of Research.
- Parrondo, J. M., Horowitz, J. M., & Sagawa, T. (2015). Thermodynamics of Information. Nature Physics.
- Rabinovich, M. I., et al. (2012). Transient dynamics: a fundamental principle of cognitive functions. Physiological Reviews.
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- Vitiello, G. (2004). The Dissipative Brain. John Benjamins.
- Wang, R. et al. (2020). Ultrasensitive thermal imaging of neural activity. PNAS.
© 2026 Luigi Usai. Licensed under CC BY 4.0.