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Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.03.22.533807Source: DataRank Database

Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0. It has been cited 3 times.

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Open Access3 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)OA: greenTopics (17)IDs (PubMed, OpenAlex)

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Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (13)

Yanlei Zhang ORCID,Jessica L. Moore ORCID,Feng Gao ORCID,Bastian Rieck ORCID,Guy Wolf ORCID

Live enrichment

PubMed:40654845 OpenAlex:https://openalex.org/W4360820285

National Institutes of Health

Grant: NIGMSR01GM135929

National Institutes of Health

Grant: R01GM130847

Grant: IIS-2047856

Grant: DMS 2327211

Grant: CISE 2403317

Fonds de Recherche du Québec - Nature et Technologies

Grant: FRQNT 343567

NIGMS NIH HHS

Grant: R01 GM135929

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/03/24/2023.03.22.533807.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.03.22.533807

repositoryhttps://doi.org/10.1101/2023.03.22.533807

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40654845

Fields of Study

Topological and Geometric Data AnalysisCell Image Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

Keywords

Persistent homologyHomology (biology)Computer scienceScatteringNeuroscienceArtificial intelligenceComputational biologyPhysicsStatistical physicsBiologyAlgorithmGeneticsOpticsGene