Ontology type: schema:ScholarlyArticle
1995-09
AUTHORSKen-ichiro Miura, Takashi Nagano
ABSTRACTA neural network model is proposed that detects the actual velocity vector of an object by using local motion signals detected by local motion detectors. First, the computational theory to obtain the actual velocity vector of a rigid object is described. Then a neural network is shown that implements the theory. The neural network model is constructed by two layers: a local velocity vector extraction layer and an integration layer. Many velocity vectors of points on a moving object are detected by local detectors in the local velocity vector extraction layer. These local velocity vectors are integrated in the integration layer in order to obtain the actual velocity vector of the object. The computational processing pathway of the proposed model is similar to the motion processing pathway of visual stimulus in the actual nervous system. More... »
PAGES4-8
http://scigraph.springernature.com/pub.10.1007/bf02332158
DOIhttp://dx.doi.org/10.1007/bf02332158
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1002515830
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/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Hosei University",
"id": "https://www.grid.ac/institutes/grid.257114.4",
"name": [
"Department of Industrial and System Engineering, College of Engineering, Hosei University, 3-7-2 Kajinocho, Koganeishi, 184, Tokyo, Japan"
],
"type": "Organization"
},
"familyName": "Miura",
"givenName": "Ken-ichiro",
"id": "sg:person.01135752122.35",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01135752122.35"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Hosei University",
"id": "https://www.grid.ac/institutes/grid.257114.4",
"name": [
"Department of Industrial and System Engineering, College of Engineering, Hosei University, 3-7-2 Kajinocho, Koganeishi, 184, Tokyo, Japan"
],
"type": "Organization"
},
"familyName": "Nagano",
"givenName": "Takashi",
"id": "sg:person.01034442456.31",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034442456.31"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/0004-3702(81)90024-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039049535"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00274887",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040688784",
"https://doi.org/10.1007/bf00274887"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00274887",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040688784",
"https://doi.org/10.1007/bf00274887"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00224859",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043342679",
"https://doi.org/10.1007/bf00224859"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1113/jphysiol.1965.sp007638",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046300786"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1113/jphysiol.1959.sp006308",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047763294"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1113/jphysiol.1974.sp010452",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051123025"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1364/josaa.4.001455",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1065165583"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1523/jneurosci.10-10-03323.1990",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1078482850"
],
"type": "CreativeWork"
}
],
"datePublished": "1995-09",
"datePublishedReg": "1995-09-01",
"description": "A neural network model is proposed that detects the actual velocity vector of an object by using local motion signals detected by local motion detectors. First, the computational theory to obtain the actual velocity vector of a rigid object is described. Then a neural network is shown that implements the theory. The neural network model is constructed by two layers: a local velocity vector extraction layer and an integration layer. Many velocity vectors of points on a moving object are detected by local detectors in the local velocity vector extraction layer. These local velocity vectors are integrated in the integration layer in order to obtain the actual velocity vector of the object. The computational processing pathway of the proposed model is similar to the motion processing pathway of visual stimulus in the actual nervous system.",
"genre": "research_article",
"id": "sg:pub.10.1007/bf02332158",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1132792",
"issn": [
"1370-4621",
"1573-773X"
],
"name": "Neural Processing Letters",
"type": "Periodical"
},
{
"issueNumber": "5",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "2"
}
],
"name": "A biologically plausible neural network for motion detection",
"pagination": "4-8",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"0bfa74aae52ada5f4d65a9c7fd2fc241e11b213c5bfcb76f27f9ac60cb9ac759"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/bf02332158"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1002515830"
]
}
],
"sameAs": [
"https://doi.org/10.1007/bf02332158",
"https://app.dimensions.ai/details/publication/pub.1002515830"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T12:57",
"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/v90/0000000365_0000000365/records_71674_00000000.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2FBF02332158"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/bf02332158'
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/bf02332158'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02332158'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02332158'
This table displays all metadata directly associated to this object as RDF triples.
94 TRIPLES
21 PREDICATES
35 URIs
19 LITERALS
7 BLANK NODES