Neural Information as an Energy-Driven Coherent System: Linking Metabolism, Regulation, and Cognition
Biological intelligence is not just about electrical signals in the brain—it is a delicate dance between information processing and energy availability. Every memory, decision, and perception consumes metabolic resources, and when energy is limited, the brain’s ability to maintain stable and adaptive information is compromised. While classical neuroscience often treats information and metabolism as separate domains, growing evidence shows that they are inseparably linked. Understanding this coupling is essential not only for basic neuroscience but also for addressing cognitive deficits, neurodegeneration, and mental resilience.
Our research introduces a systems-level framework in which neural information is modeled as an energy-driven coherent process. Instead of viewing energy as a passive substrate, we consider it a primary driver of neural computation, influencing how efficiently information is encoded, transmitted, and stabilized across neural networks. The model integrates three dynamically coupled layers:
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Energetic Layer: Governs the generation, distribution, and buffering of metabolic resources, including ATP availability, glucose oxidation, and mitochondrial output.
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Informational Layer: Encodes, transmits, and integrates signals through spikes, oscillatory patterns, and network connectivity.
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Regulatory Layer: Monitors coherence between energy and information, and implements corrective feedback via neuromodulation, vascular coupling, and synaptic scaling.
At the heart of this framework is a dimensionless coherence parameter (C = I/E), quantifying the efficiency of energy-to-information conversion. Here, I represents the integrated informational load—the complexity, volume, and diversity of neural signaling—while E captures available bioenergetic capacity. A system operating within an optimal coherence window (approximately 0.5–0.8) balances energy usage with informational demand. Deviations beyond this range indicate underutilization, overload, or risk of collapse, providing a quantitative measure to link metabolic state with cognitive function.
Through simulations, we observed several fundamental principles:
- Metabolic constraints shape information processing: When energy is limited, the system downscales informational load, preserving core functions but reducing flexibility and adaptability.
- Overload leads to instability: Excessive informational demand relative to available energy pushes the system into unstable regimes, analogous to cognitive fatigue or pathological states.
- Neuromodulators stabilize coherence: Noradrenaline enhances signal-to-noise ratio, dopamine adjusts network flexibility, and acetylcholine modulates cortical gain, together maintaining C within the functional range.
Our framework bridges classical concepts such as efficient coding, the Free Energy Principle, and the information bottleneck with energetic constraints, offering testable predictions across scales. For example, we propose that fluctuations in ATP availability, as measured by imaging or biosensors, correlate with network-level informational efficiency. This opens pathways to experimental validation using fMRI, PET, or electrophysiological recordings.
From a translational perspective, recognizing energy as a driver of neural information has significant implications. It provides mechanistic insight into why metabolic stress—whether through hypoxia, mitochondrial dysfunction, or nutrient limitation—leads to cognitive impairment. It also highlights strategies to enhance brain resilience: optimizing energy availability, targeting neuromodulatory pathways, or modulating informational load through training and adaptive tasks.
To illustrate our framework visually, we provide a three-layer schematic. The energetic layer supplies fuel to the informational layer, which generates neural signals. The regulatory layer monitors coherence and implements corrections. Color-coded zones indicate functional stability: green for optimal coherence, yellow for overload, red for collapse, and blue for conservation under low-energy conditions. Time-series simulations show how energy and information interact dynamically under normal, stressed, and adaptive scenarios.
We anticipate that this approach will be particularly useful for researchers working on neurodegeneration, cognitive enhancement, or systems-level neuroscience. By explicitly linking metabolism with information processing, it provides a quantitative lens for understanding brain function, plasticity, and failure. Moreover, it sets the stage for integrating molecular, cellular, and network-level studies under a common energetic framework.
In sum, neural information is forged in the crucible of energy, and recognizing this coupling is essential for understanding cognition and disease. Our energy-driven coherence framework offers a rigorously defined, experimentally accessible, and conceptually integrative model, bridging theory with practical applications for neuroscience and medicine.
Keywords / Topics
- Neural Information Processing
- Bioenergetics
- Systems Biology
- Coherence in Neural Systems
- Cognitive Resilience
- Neuromodulation
- Metabolic Constraints on Brain Function