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Input correlations impede suppression of chaos and learning in balanced firing-rate networks

PLoS Computational Biology(2022)10.1371/journal.pcbi.1010590Source: DataRank Database

Input correlations impede suppression of chaos and learning in balanced firing-rate networks is a research paper published in PLoS Computational Biology (2022). On theSindex it has a DataRank of 0. It has been cited 13 times.

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Open Access13 citations

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Linked data & code

Input correlations impede suppression of chaos and learning in balanced rate networks · arXiv

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (8 grants)Impact (FWCI 1.14)OA: goldTopics (49)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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Authors (5)

Alessandro Ingrosso ORCID,Ramin Khajeh ORCID,Sven Goedeke ORCID,L. F. Abbott ORCID,Rainer Engelken ORCID

Live enrichment

PubMed:36469504 PMC:PMC9754616 OpenAlex:https://openalex.org/W4310703724

National Science Foundation

Grant: DBI-1707398

NIH

Grant: U19NS107613-01

Bernstein Award 2014

Grant: 01GQ171

NINDS NIH HHS

Grant: U19 NS107613

National Institutes of Health

Grant: 5U19NS107613-03

Understanding V1 circuit dynamics and computations

National Science Foundation

Grant: 1707398

NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center

Swartz Foundation

Gatsby Charitable Foundation

FWCI

1.14

Citation Percentile

0.8%

Citation Trend

2022

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010590&type=printable

publisherhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010590&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1010590

journalhttps://doi.org/10.1371/journal.pcbi.1010590

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36469504

Fields of Study

Neural dynamics and brain functionstochastic dynamics and bifurcationNeural Networks and Applications0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

Action PotentialsLearningModels, NeurologicalNerve NetNeurons

Keywords

Lyapunov exponentChaoticArtificial neural networkUncorrelatedComputer scienceControl theory (sociology)Feature (linguistics)Coupling (piping)Biological neural networkNetwork dynamicsMathematicsStatistical physicsArtificial intelligencePhysicsControl (management)Machine learningStatisticsNeuronsFOS: Computer and information sciencesComputational NeuroscienceComputer Science - Machine LearningQH301-705.5Models, NeurologicalAction PotentialsFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksNonlinear Sciences - Chaotic DynamicsNetworks, dynamical systemsMachine Learning (cs.LG)Quantitative Biology - Neurons and CognitionFOS: Biological sciencesLearningNeurons and Cognition (q-bio.NC)Biology (General)Nerve NetChaotic Dynamics (nlin.CD)Research Article

Input correlations impede suppression of chaos and learning in balanced rate networks

10.48550/arxiv.2201.09916arXiv