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Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.09.14.507926Source: DataRank Database

Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 15 times.

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Open Access15 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (4 grants)OA: greenTopics (29)IDs (PubMed, OpenAlex)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (6)

Marta Gajowa ORCID,Benjamin Antin ORCID,Masato Sadahiro ORCID,Hillel Adesnik ORCID,Liam Paninski ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4296149771

National Institutes of Health

Grant: 1RF1MH120680-01

High-throughput Physiological Micro-connectivity Mapping in Vivo

National Institutes of Health

Grant: 5K99NS135649-02

Computational Methods for Precise Holographic Control and Mapping of Neural Circuits

National Institutes of Health

Grant: 1U19NS107613-01

Understanding V1 circuit dynamics and computations

National Science Foundation

Grant: 1707398

NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center

greenLicense: other-oa

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/09/17/2022.09.14.507926.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.09.14.507926

repositoryhttps://doi.org/10.1101/2022.09.14.507926

https://doi.org/10.1038/s41593-025-02053-7

Fields of Study

Neural dynamics and brain functionAdvanced Memory and Neural ComputingAdvanced Fluorescence Microscopy Techniques0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Postsynaptic potentialOptogeneticsComputer scienceNeuroscienceModels of neural computationArtificial neural networkStimulus (psychology)Biological neural networkArtificial intelligenceMachine learningChemistryBiologyNeuronsModels, NeurologicalHolographyMiceTechnical ReportDeep LearningSynapsesAnimalsLearningNerve NetAlgorithms