ArticleJournal of neural engineering2020
Network structure of cascading neural systems predicts stimulus propagation and recovery.
Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Frontotemporal network interactions causally support rapid concreteness judgments during reading.PLoS biology · 2026Article
- Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024Review
- Article
- Recurrent activity in neuronal avalanches.Scientific reports · 2023Article
- Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome.Science advances · 2022Article
- Dynamic representations in networked neural systems.Nature neuroscience · 2020Review
- Models of communication and control for brain networks: distinctions, convergence, and future outlook.Network neuroscience (Cambridge, Mass.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveMany neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown. APPROACH: Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. MAIN
resultsIn particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. SIGNIFICANCE: Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.
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Registered trials
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.