ArticlePLoS computational biology2021
Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Sampling bias corrections for discrete and Gaussian partial information decompositions.Patterns (New York, N.Y.) · 2026Article
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- MINT: A toolbox for the analysis of multivariate neural information coding and transmission.PLoS computational biology · 2025Article
- Time-varying synergy/redundancy dominance in the human cerebral cortex.Journal of physics. Complexity · 2025Article
- Unveiling complex patterns: An information-theoretic approach to high-order behaviors in microarray data.PloS one · 2025Article
- A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.eLife · 2024Article
- A Partial Information Decomposition for Multivariate Gaussian Systems Based on Information Geometry.Entropy (Basel, Switzerland) · 2024Article
- Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information.bioRxiv : the preprint server for biology · 2024Article
- Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.PLoS computational biology · 2024Article
- A Measure of Synergy Based on Union Information.Entropy (Basel, Switzerland) · 2024Article
- Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex.Brain informatics · 2023Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.PloS one · 2023Article
- A Comparison of Partial Information Decompositions Using Data from Real and Simulated Layer 5b Pyramidal Cells.Entropy (Basel, Switzerland) · 2022Article
- Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.Entropy (Basel, Switzerland) · 2022Review
- Conservative significance testing of tripartite statistical relations in multivariate neural data.Network neuroscience (Cambridge, Mass.) · 2022Article
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4 authors.
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Abstract
The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration-a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration-determined from partial information decomposition-varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.
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