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Learning low-dimensional generalizable natural features from retina using a U-net

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.11.02.514893Source: DataRank Database

Learning low-dimensional generalizable natural features from retina using a U-net is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Open Access2 citations

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Enrichment:Funding (5 grants)OA: greenTopics (23)IDs (PubMed, PMC, 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)

Benjamin Hoshal ORCID,Elizabeth A de Laittre,Olivier Marre ORCID,Michael J. Berry ORCID,Stephanie E. Palmer ORCID

Live enrichment

PubMed:37362058 PMC:PMC10289798 OpenAlex:https://openalex.org/W4308304665

European Research Council

Grant: 101045253

A perturbative approach to model retinal processing of natural scenes

NIBIB NIH HHS

Grant: R01 EB026943

National Science Foundation

Grant: 1734030

PFC: Center for the Physics of Biological Function

National Institutes of Health

Grant: 5R01EB026943-03

Coarse-graining approaches to networks, learning, and behavior

National Science Foundation

Grant: 1746045

Graduate Research Fellowship Program (GRFP)

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/11/03/2022.11.02.514893.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.11.02.514893

repositoryhttps://doi.org/10.1101/2022.11.02.514893

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37362058

Fields of Study

Neural dynamics and brain functionRetinal Development and DisordersAdvanced Memory and Neural Computing0301 basic medicine03 medical and health sciences0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceEncoding (memory)Artificial intelligenceScene statisticsRepresentation (politics)Natural (archaeology)EncoderAutoencoderNeural codingComputer visionPattern recognition (psychology)PerceptionDeep learningNeurosciencePsychologyBiology