ArticleFrontiers in neuroscience2019
Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An astro-neural-field model with application to cortical spreading depolarization.Journal of computational neuroscience · 2026Article
- From artificial to organic: Rethinking the roots of intelligence for digital health.PLOS digital health · 2025Article
- Editorial: Brain-inspired intelligence: the deep integration of brain science and artificial intelligence.Frontiers in computational neuroscience · 2025Article
- A systematic approach to brain dynamics: cognitive evolution theory of consciousness.Cognitive neurodynamics · 2023Article
- Probing individual-level structural atrophy in frontal glioma patients.Neurosurgical review · 2022Article
- Energy efficiency and coding of neural network.Frontiers in neuroscience · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.
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