Statistical Estimation of Non-uniform Distribution of Dendritic Membrane Properties View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Toshiaki Omori , Toru Aonishi , Masato Okada

ABSTRACT

We propose a statistical method for estimating the spatial distribution of membrane properties that are non-uniformly distributed over dendrites from partially observable noisy data. We used the Bayesian statistical approach to extract the hidden but substantial information about the distribution of membrane properties over the dendrites. Simulated data showed that the proposed method can simultaneously estimate the distribution of membrane properties and the distribution of membrane potentials, even from partially observable noisy data. More... »

PAGES

649-655

References to SciGraph publications

  • 2008-03. Pyramidal neurons: dendritic structure and synaptic integration in NATURE REVIEWS NEUROSCIENCE
  • Book

    TITLE

    Advances in Cognitive Neurodynamics (III)

    ISBN

    978-94-007-4791-3
    978-94-007-4792-0

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-94-007-4792-0_87

    DOI

    http://dx.doi.org/10.1007/978-94-007-4792-0_87

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1025878783


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Statistics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Kobe University", 
              "id": "https://www.grid.ac/institutes/grid.31432.37", 
              "name": [
                "Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe\u00a0Hyogo, 657-8501, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Omori", 
            "givenName": "Toshiaki", 
            "id": "sg:person.015721411305.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015721411305.19"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Tokyo Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.32197.3e", 
              "name": [
                "Department of Computational Intelligence and Systems, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama\u00a0Kanagawa, 226-8502, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Aonishi", 
            "givenName": "Toru", 
            "id": "sg:person.0720534144.54", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0720534144.54"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Tokyo", 
              "id": "https://www.grid.ac/institutes/grid.26999.3d", 
              "name": [
                "Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa\u00a0Chiba, 277-8561, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Okada", 
            "givenName": "Masato", 
            "id": "sg:person.010320363011.18", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010320363011.18"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1126/science.290.5492.739", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002575157"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neures.2009.01.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002604344"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neures.2009.01.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002604344"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.brainres.2006.09.095", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018377043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrn2286", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032885141", 
              "https://doi.org/10.1038/nrn2286"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2013", 
        "datePublishedReg": "2013-01-01", 
        "description": "We propose a statistical method for estimating the spatial distribution of membrane properties that are non-uniformly distributed over dendrites from partially observable noisy data. We used the Bayesian statistical approach to extract the hidden but substantial information about the distribution of membrane properties over the dendrites. Simulated data showed that the proposed method can simultaneously estimate the distribution of membrane properties and the distribution of membrane potentials, even from partially observable noisy data.", 
        "editor": [
          {
            "familyName": "Yamaguchi", 
            "givenName": "Yoko", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-94-007-4792-0_87", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.6031841", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": {
          "isbn": [
            "978-94-007-4791-3", 
            "978-94-007-4792-0"
          ], 
          "name": "Advances in Cognitive Neurodynamics (III)", 
          "type": "Book"
        }, 
        "name": "Statistical Estimation of Non-uniform Distribution of Dendritic Membrane Properties", 
        "pagination": "649-655", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-94-007-4792-0_87"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7b49ef96f53fd8dfec81c832438ac15f0db3653a2fec2c7c77e0486f0dbdc704"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1025878783"
            ]
          }
        ], 
        "publisher": {
          "location": "Dordrecht", 
          "name": "Springer Netherlands", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-94-007-4792-0_87", 
          "https://app.dimensions.ai/details/publication/pub.1025878783"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T00:49", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000259.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-94-007-4792-0_87"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-94-007-4792-0_87'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-94-007-4792-0_87'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-94-007-4792-0_87'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-94-007-4792-0_87'


     

    This table displays all metadata directly associated to this object as RDF triples.

    100 TRIPLES      23 PREDICATES      31 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-94-007-4792-0_87 schema:about anzsrc-for:01
    2 anzsrc-for:0104
    3 schema:author N27de1bb096644436bc7e9a2a28736a3e
    4 schema:citation sg:pub.10.1038/nrn2286
    5 https://doi.org/10.1016/j.brainres.2006.09.095
    6 https://doi.org/10.1016/j.neures.2009.01.012
    7 https://doi.org/10.1126/science.290.5492.739
    8 schema:datePublished 2013
    9 schema:datePublishedReg 2013-01-01
    10 schema:description We propose a statistical method for estimating the spatial distribution of membrane properties that are non-uniformly distributed over dendrites from partially observable noisy data. We used the Bayesian statistical approach to extract the hidden but substantial information about the distribution of membrane properties over the dendrites. Simulated data showed that the proposed method can simultaneously estimate the distribution of membrane properties and the distribution of membrane potentials, even from partially observable noisy data.
    11 schema:editor N54394ce70cae484e857aa5d2b9f547fd
    12 schema:genre chapter
    13 schema:inLanguage en
    14 schema:isAccessibleForFree false
    15 schema:isPartOf Nbd8d4b9ec725460cae3508748d5c0334
    16 schema:name Statistical Estimation of Non-uniform Distribution of Dendritic Membrane Properties
    17 schema:pagination 649-655
    18 schema:productId N056697957a774942bf67e28927127d75
    19 N0d92c315aa974a51a012e2b4e7c03fe9
    20 Ncee14b7bfc7649889ef20bcade983413
    21 schema:publisher N2a2ac5ad432d42769c9a98fd1cce614c
    22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025878783
    23 https://doi.org/10.1007/978-94-007-4792-0_87
    24 schema:sdDatePublished 2019-04-16T00:49
    25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    26 schema:sdPublisher N9b2ac57ffbcf4c4492ea364ef695843e
    27 schema:url http://link.springer.com/10.1007/978-94-007-4792-0_87
    28 sgo:license sg:explorer/license/
    29 sgo:sdDataset chapters
    30 rdf:type schema:Chapter
    31 N056697957a774942bf67e28927127d75 schema:name readcube_id
    32 schema:value 7b49ef96f53fd8dfec81c832438ac15f0db3653a2fec2c7c77e0486f0dbdc704
    33 rdf:type schema:PropertyValue
    34 N0d92c315aa974a51a012e2b4e7c03fe9 schema:name doi
    35 schema:value 10.1007/978-94-007-4792-0_87
    36 rdf:type schema:PropertyValue
    37 N189a5c2c9b034f4bac14a4441e7fee45 schema:familyName Yamaguchi
    38 schema:givenName Yoko
    39 rdf:type schema:Person
    40 N27de1bb096644436bc7e9a2a28736a3e rdf:first sg:person.015721411305.19
    41 rdf:rest N7b28a843a01a4e7d85e4376bdbddaf07
    42 N2a2ac5ad432d42769c9a98fd1cce614c schema:location Dordrecht
    43 schema:name Springer Netherlands
    44 rdf:type schema:Organisation
    45 N54394ce70cae484e857aa5d2b9f547fd rdf:first N189a5c2c9b034f4bac14a4441e7fee45
    46 rdf:rest rdf:nil
    47 N7b28a843a01a4e7d85e4376bdbddaf07 rdf:first sg:person.0720534144.54
    48 rdf:rest Nc0d45001becd4a848657e8e02fea3e3c
    49 N9b2ac57ffbcf4c4492ea364ef695843e schema:name Springer Nature - SN SciGraph project
    50 rdf:type schema:Organization
    51 Nbd8d4b9ec725460cae3508748d5c0334 schema:isbn 978-94-007-4791-3
    52 978-94-007-4792-0
    53 schema:name Advances in Cognitive Neurodynamics (III)
    54 rdf:type schema:Book
    55 Nc0d45001becd4a848657e8e02fea3e3c rdf:first sg:person.010320363011.18
    56 rdf:rest rdf:nil
    57 Ncee14b7bfc7649889ef20bcade983413 schema:name dimensions_id
    58 schema:value pub.1025878783
    59 rdf:type schema:PropertyValue
    60 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    61 schema:name Mathematical Sciences
    62 rdf:type schema:DefinedTerm
    63 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    64 schema:name Statistics
    65 rdf:type schema:DefinedTerm
    66 sg:grant.6031841 http://pending.schema.org/fundedItem sg:pub.10.1007/978-94-007-4792-0_87
    67 rdf:type schema:MonetaryGrant
    68 sg:person.010320363011.18 schema:affiliation https://www.grid.ac/institutes/grid.26999.3d
    69 schema:familyName Okada
    70 schema:givenName Masato
    71 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010320363011.18
    72 rdf:type schema:Person
    73 sg:person.015721411305.19 schema:affiliation https://www.grid.ac/institutes/grid.31432.37
    74 schema:familyName Omori
    75 schema:givenName Toshiaki
    76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015721411305.19
    77 rdf:type schema:Person
    78 sg:person.0720534144.54 schema:affiliation https://www.grid.ac/institutes/grid.32197.3e
    79 schema:familyName Aonishi
    80 schema:givenName Toru
    81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0720534144.54
    82 rdf:type schema:Person
    83 sg:pub.10.1038/nrn2286 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032885141
    84 https://doi.org/10.1038/nrn2286
    85 rdf:type schema:CreativeWork
    86 https://doi.org/10.1016/j.brainres.2006.09.095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018377043
    87 rdf:type schema:CreativeWork
    88 https://doi.org/10.1016/j.neures.2009.01.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002604344
    89 rdf:type schema:CreativeWork
    90 https://doi.org/10.1126/science.290.5492.739 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002575157
    91 rdf:type schema:CreativeWork
    92 https://www.grid.ac/institutes/grid.26999.3d schema:alternateName University of Tokyo
    93 schema:name Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa Chiba, 277-8561, Japan
    94 rdf:type schema:Organization
    95 https://www.grid.ac/institutes/grid.31432.37 schema:alternateName Kobe University
    96 schema:name Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe Hyogo, 657-8501, Japan
    97 rdf:type schema:Organization
    98 https://www.grid.ac/institutes/grid.32197.3e schema:alternateName Tokyo Institute of Technology
    99 schema:name Department of Computational Intelligence and Systems, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama Kanagawa, 226-8502, Japan
    100 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...