ArticleProceedings of the National Academy of Sciences of the United States of America2023
Network inference from short, noisy, low time-resolution, partial measurements: Application to
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Learning to learn ecosystems from limited data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Role of connectivity anisotropies in the dynamics of cultured neuronal networks.PLoS computational biology · 2025Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Brain-inspired wiring economics for artificial neural networks.PNAS nexus · 2025Article
- Light evokes stereotyped global brain dynamics in Caenorhabditis elegans.Current biology : CB · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from
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