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Thermodynamic constraints as organizing principles of neural efficiency and cognitive architecture.

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Τίτλος: Thermodynamic constraints as organizing principles of neural efficiency and cognitive architecture.
Συγγραφείς: Torres-Martínez S; Escuela de Idiomas, Universidad de Antioquia, Medellín, Antioquia, Colombia. Electronic address: salberto.torres@udea.edu.co.
Πηγή: Bio Systems [Biosystems] 2026 Oct; Vol. 268, pp. 105921. Date of Electronic Publication: 2026 Aug 13.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 0430773 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8324 (Electronic) Linking ISSN: 03032647 NLM ISO Abbreviation: Biosystems Subsets: MEDLINE
Imprint Name(s): Publication: Limerick : Elsevier Science Ireland
Original Publication: Amsterdam, North-Holland Pub. Co.
Ιατρικοί όροι (MeSH): Neurons*/physiology , Cognition*/physiology , Brain*/physiology , Thermodynamics* , Models, Neurological*, Synapses/physiology ; Adenosine Triphosphate/metabolism ; Humans ; Animals
Περίληψη: Biological neural systems sustain adaptive cognition under persistent and severe metabolic constraints. This paper develops a multi-scale thermodynamic framework in which those constraints function as organizing principles of neural efficiency and cognitive architecture, addressing four distinct shortcomings of prior accounts. First, I replace the widely cited "2 bits per synapse" convention with a probabilistic model grounded in the Bernoulli statistics of downstream neuron firing, yielding a substantially more empirically defensible efficiency ratio of approximately 3.4 × 106 times the Landauer limit. Second, I close the quantitative gap between per-synapse ATP chemistry and the brain's globally measured 20 W power budget through an explicit multi-scale bridge. Third, sparse coding, predictive processing, and cross-frequency coupling are derived as Lagrangian solutions to a single metabolic optimisation functional rather than merely described as consistent with energetic principles. Fourth, neuromorphic efficiency comparisons are updated and standardised to an energy-per-bit metric for Intel Loihi 2, BrainScaleS-2, and SpiNNaker2, and are extended to two compute-in-memory architectures - the charge-recycling array processor of Karakiewicz et al. (2012) and the RRAM-based NeuRRAM chip of Wan et al. (2022) - both of which approach or exceed biological synaptic efficiency. Each coding strategy generates testable, quantitatively specified predictions that admit principled rejection. The framework positions metabolic pressure not as an engineering detail extrinsic to neuroscience but as a constitutive selective force in the evolutionary shaping of neural computation.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interests The author declares no conflicts of interest.
Contributed Indexing: Keywords: Landauer limit; Metabolic optimisation; Neural efficiency; Neuromorphic computing; Predictive processing; Stochastic thermodynamics; Thermodynamic constraints
Substance Nomenclature: 8L70Q75FXE (Adenosine Triphosphate)
Entry Date(s): Date Created: 20260813 Date Completed: 20260906 Latest Revision: 20260906
Update Code: 20260907
DOI: 10.1016/j.biosystems.2026.105921
PMID: 42595294
Βάση Δεδομένων: MEDLINE
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  Data: Biological neural systems sustain adaptive cognition under persistent and severe metabolic constraints. This paper develops a multi-scale thermodynamic framework in which those constraints function as organizing principles of neural efficiency and cognitive architecture, addressing four distinct shortcomings of prior accounts. First, I replace the widely cited "2 bits per synapse" convention with a probabilistic model grounded in the Bernoulli statistics of downstream neuron firing, yielding a substantially more empirically defensible efficiency ratio of approximately 3.4 × 10<superscript>6</superscript> times the Landauer limit. Second, I close the quantitative gap between per-synapse ATP chemistry and the brain's globally measured 20 W power budget through an explicit multi-scale bridge. Third, sparse coding, predictive processing, and cross-frequency coupling are derived as Lagrangian solutions to a single metabolic optimisation functional rather than merely described as consistent with energetic principles. Fourth, neuromorphic efficiency comparisons are updated and standardised to an energy-per-bit metric for Intel Loihi 2, BrainScaleS-2, and SpiNNaker2, and are extended to two compute-in-memory architectures - the charge-recycling array processor of Karakiewicz et al. (2012) and the RRAM-based NeuRRAM chip of Wan et al. (2022) - both of which approach or exceed biological synaptic efficiency. Each coding strategy generates testable, quantitatively specified predictions that admit principled rejection. The framework positions metabolic pressure not as an engineering detail extrinsic to neuroscience but as a constitutive selective force in the evolutionary shaping of neural computation.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.)
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