ArticleFrontiers in computational neuroscience2018
Effects of Metabolic Energy on Synaptic Transmission and Dendritic Integration in Pyramidal Neurons.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Direct energy consumption by inhibitory GABAFrontiers in neuroscience · 2026Article
- Mitochondrial Dysfunction: A Key Player in Brain Aging and Diseases.Current issues in molecular biology · 2024Review
- The transcriptome of rat hippocampal subfields.IBRO neuroscience reports · 2022Article
- Markers of Hypoxia and Metabolism Correlate With Cell Differentiation in Retina and Lens Development.Frontiers in ophthalmology · 2022Article
- A Metabolic Landscape for Maintaining Retina Integrity and Function.Frontiers in molecular neuroscience · 2021Review
- Resting and stimulated mouse rod photoreceptors show distinct patterns of vesicle release at ribbon synapses.The Journal of general physiology · 2020Article
- Article
- Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases.Frontiers in neuroscience · 2019Article
- Constraints of Metabolic Energy on the Number of Synaptic Connections of Neurons and the Density of Neuronal Networks.Frontiers in computational neuroscience · 2018Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
As a sophisticated computing unit, the pyramidal neuron requires sufficient metabolic energy to fuel its powerful computational capabilities. However, the majority of previous works focus on nonlinear integration and energy consumption in individual pyramidal neurons but seldom on the effects of metabolic energy on synaptic transmission and dendritic integration. Here, we developed biologically plausible models to simulate the synaptic transmission and dendritic integration of pyramidal neurons, exploring the relations between synaptic transmission and metabolic energy and between dendritic integration and metabolic energy. We find that synaptic energy not only drives synaptic vesicle cycle, but also participates in the regulation of this cycle. Release probability of synapses adapts to synaptic energy levels by regulating the speed of synaptic vesicle cycle. Besides, we also find that to match neural energy levels, only a part of the synapses receive presynaptic signals during a given period so that neurons have a low action potential frequency. That is, the number of simultaneously active synapses over a period of time should be adapted to neural energy levels.
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