ReviewJournal of the Royal Society, Interface2017
Generative models for network neuroscience: prospects and promise.
Review in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Human cortical networks trade communication efficiency for computational reliability.Science advances · 2026Article
- Analytically tractable model of synaptic crowding explains emergent small-world structure and network dynamics.Scientific reports · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
- Building the connectome of a small brain with a simple stochastic developmental generative model.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Exploring the transmission of cognitive task information through optimal brain pathways.PLoS computational biology · 2025Article
- Premature birth changes wiring constraints in neonatal structural brain networks.Nature communications · 2025Article
- Metric structural human connectomes: Localization and multifractality of eigenmodes.Network neuroscience (Cambridge, Mass.) · 2025Article
- Coming up short: Generative network models fail to accurately capture long-range connectivity.Network neuroscience (Cambridge, Mass.) · 2025Article
- Biological detail and graph structure in network neuroscience.Frontiers in network physiology · 2025Review
- Distributed network flows generate localized category selectivity in human visual cortex.PLoS computational biology · 2024Article
- A shifting role of thalamocortical connectivity in the emergence of cortical functional organization.Nature neuroscience · 2024Article
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Article
- Recent Progress in Brain Network Models for Medical Applications: A Review.Health data science · 2024Review
- Towards a biologically annotated brain connectome.Nature reviews. Neuroscience · 2023Review
- Early adversity changes the economic conditions of mouse structural brain network organization.Developmental psychobiology · 2023Article
- Simulation-based inference for efficient identification of generative models in computational connectomics.PLoS computational biology · 2023Article
- From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks.The Journal of physiology · 2023Article
- Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity.Science advances · 2022Article
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2 authors.
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Abstract
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.