ArticleNeural plasticity2017
Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model.
Article in Neural plasticity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Baseline electrophysiology versus oxidative metabolism as traits of inter-network differences in neurometabolic association.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
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- Direct energy consumption by inhibitory GABAFrontiers in neuroscience · 2026Article
- Less is more: Aesthetic liking is inversely related to metabolic expense by the visual system.PNAS nexus · 2025Article
- A genetic algorithm for self-supervised models of oscillatory neurodynamics.bioRxiv : the preprint server for biology · 2025Article
- A flexible spiking hair sensillum for ultralow power density noncontact perception.Science advances · 2025Article
- Consciousness and Energy Processing in Neural Systems.Brain sciences · 2024Article
- Mapping between cognitive theories and psycho-physiological models of attention system performance.Cerebral cortex (New York, N.Y. : 1991) · 2023Review
- Size-dependent differences in mitochondrial volume density in phrenic motor neurons.Journal of applied physiology (Bethesda, Md. : 1985) · 2023Article
- Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model.Scientific reports · 2022Article
- Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models.Frontiers in cellular neuroscience · 2022Article
- TheFrontiers in psychology · 2022Article
- Biophysical mechanism of the interaction between default mode network and working memory network.Cognitive neurodynamics · 2021Article
- Energy features in spontaneous up and down oscillations.Cognitive neurodynamics · 2021Article
- The Organization of the Sinoatrial Node Microvasculature Varies Regionally to Match Local Myocyte Excitability.Function (Oxford, England) · 2021Article
- Voluntary Behavior and Training Conditions ModulateFrontiers in neuroscience · 2021Article
- Ionic channel blockage in stochastic Hodgkin-Huxley neuronal model driven by multiple oscillatory signals.Cognitive neurodynamics · 2020Article
- High-Frequency Synchronization Improves Firing Rate Contrast and Information Transmission Efficiency in E/I Neuronal Networks.Neural plasticity · 2020Article
- Upper Limit on the Thermodynamic Information Content of an Action Potential.Frontiers in computational neuroscience · 2020Article
- Spontaneous electromagnetic induction promotes the formation of economical neuronal network structure via self-organization process.Scientific reports · 2019Article
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3 authors.
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Abstract
Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors.
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