ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2018
Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Restricted Network Reconstruction from Time Series via Dempster-Shafer Evidence Theory.Entropy (Basel, Switzerland) · 2026Article
- CeDNe: A multi-scale computational framework for modeling structure-function relationships in thebioRxiv : the preprint server for biology · 2025Article
- Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling.Cognitive neurodynamics · 2024Article
- Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm.PLoS computational biology · 2022Article
- The transcription factor unc-130/FOXD3/4 contributes to the biphasic calcium response required to optimize avoidance behavior.Scientific reports · 2022Article
- Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study.Frontiers in systems neuroscience · 2022Article
- A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome.PloS one · 2021Article
- Computing Temporal Sequences Associated With Dynamic Patterns on theFrontiers in systems neuroscience · 2021Article
- Nonlinear Control in the NematodeFrontiers in computational neuroscience · 2020Article
- Novel Technological Advances in Functional Connectomics inJournal of developmental biology · 2019Review
- Neural Interactome: Interactive Simulation of a Neuronal System.Frontiers in computational neuroscience · 2019Article
- Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift DuringFrontiers in molecular biosciences · 2019Article
- Connectome to behaviour: modellingPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the
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