ArticleScientific reports2021
On the structural connectivity of large-scale models of brain networks at cellular level.
Article in Scientific reports, 2021. 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.
- Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics.Communications biology · 2026Article
- Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- A novel integration of brain structural and functional connectivity for identifying traumatic brain injury induced perturbations.Journal of neuroscience methods · 2025Article
- Integrating multimodal data to understand cortical circuit architecture and function.Nature neuroscience · 2025Review
- CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.bioRxiv : the preprint server for biology · 2025Article
- Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.PLoS biology · 2024Article
- A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits.Scientific reports · 2022Article
- The role of network connectivity on epileptiform activity.Scientific reports · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain's structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model's connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.
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