Self-organization of the velocity selectivity of a directionally selective neural network View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1995-10

AUTHORS

Ken-ichiro Miura, Koji Kurata, Takashi Nagano

ABSTRACT

We first present a mathematical analysis of the relation between the parameters and the behavior of the basic module in the proposed neural network model for visual motion detection. Based on the analytical results, a learning rule is put forth that can develop velocity selectivity of directionally selective cells in the basic module. The learning rule is furthermore introduced into the total model called a 'mass model', which is constructed with many basic modules. Numerical simulation results showed that each basic module in the mass model learned in a self-organizing manner to acquire selectivity for the velocity of an input stimulus. The proposed learning rule would be plausible in the actual nervous system in that it is simple and can be described with only local information. More... »

PAGES

401-407

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00201474

DOI

http://dx.doi.org/10.1007/bf00201474

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018856138

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/7578477


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/2008/0801", 
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Motion Perception", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nerve Net", 
        "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 Kajino-cho, Koganei-shi, 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": "Osaka University", 
          "id": "https://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Biological Engineering, Faculty of Engineering Science, Osaka University, 1-1 Machikaneyama-cho, Toyonaka-shi, 560, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kurata", 
        "givenName": "Koji", 
        "id": "sg:person.01204065322.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204065322.14"
        ], 
        "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 Kajino-cho, Koganei-shi, 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/10.3169/itej1978.33.479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008924379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(87)90118-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027016738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(87)90118-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027016738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00337445", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027058937", 
          "https://doi.org/10.1007/bf00337445"
        ], 
        "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/10.1016/0006-8993(76)90313-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045872409"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0006-8993(76)90313-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045872409"
        ], 
        "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/10.1016/0893-6080(90)90045-m", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046399255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0893-6080(90)90045-m", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046399255"
        ], 
        "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/10.1068/p140105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058162695"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1068/p140105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058162695"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.1986.55.6.1308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079508406"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.1983.49.5.1127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1081999661"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.1993.714237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086272575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.1993.714237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086272575"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1995-10", 
    "datePublishedReg": "1995-10-01", 
    "description": "We first present a mathematical analysis of the relation between the parameters and the behavior of the basic module in the proposed neural network model for visual motion detection. Based on the analytical results, a learning rule is put forth that can develop velocity selectivity of directionally selective cells in the basic module. The learning rule is furthermore introduced into the total model called a 'mass model', which is constructed with many basic modules. Numerical simulation results showed that each basic module in the mass model learned in a self-organizing manner to acquire selectivity for the velocity of an input stimulus. The proposed learning rule would be plausible in the actual nervous system in that it is simple and can be described with only local information.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf00201474", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1081741", 
        "issn": [
          "0340-1200", 
          "1432-0770"
        ], 
        "name": "Biological Cybernetics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "73"
      }
    ], 
    "name": "Self-organization of the velocity selectivity of a directionally selective neural network", 
    "pagination": "401-407", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "59d7f853a79f7924cc821272ad1505c8142195f63eccd73f38f4b4119af9f5d3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "7578477"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7502533"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00201474"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018856138"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00201474", 
      "https://app.dimensions.ai/details/publication/pub.1018856138"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:02", 
    "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/v90/0000000371_0000000371/records_130831_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF00201474"
  }
]
 

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/bf00201474'

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/bf00201474'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00201474'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00201474'


 

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

148 TRIPLES      21 PREDICATES      47 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00201474 schema:about N56370f05ab684021835dab14ccb3733e
2 N7a527cff98eb4e4c81b964af702d15a7
3 N8021fdfa226c49c6bece7fab6e0fb34f
4 N84eba39062e1420ab8ec6df1914ab92d
5 Na1aaeadb0dbe431e88b68913fc3be64b
6 anzsrc-for:08
7 anzsrc-for:0801
8 schema:author N462dc2a069e741c783b71991b192a66f
9 schema:citation sg:pub.10.1007/bf00224859
10 sg:pub.10.1007/bf00274887
11 sg:pub.10.1007/bf00337445
12 https://doi.org/10.1016/0006-8993(76)90313-9
13 https://doi.org/10.1016/0042-6989(87)90118-0
14 https://doi.org/10.1016/0893-6080(90)90045-m
15 https://doi.org/10.1068/p140105
16 https://doi.org/10.1109/ijcnn.1993.714237
17 https://doi.org/10.1113/jphysiol.1965.sp007638
18 https://doi.org/10.1113/jphysiol.1974.sp010452
19 https://doi.org/10.1152/jn.1983.49.5.1127
20 https://doi.org/10.1152/jn.1986.55.6.1308
21 https://doi.org/10.3169/itej1978.33.479
22 schema:datePublished 1995-10
23 schema:datePublishedReg 1995-10-01
24 schema:description We first present a mathematical analysis of the relation between the parameters and the behavior of the basic module in the proposed neural network model for visual motion detection. Based on the analytical results, a learning rule is put forth that can develop velocity selectivity of directionally selective cells in the basic module. The learning rule is furthermore introduced into the total model called a 'mass model', which is constructed with many basic modules. Numerical simulation results showed that each basic module in the mass model learned in a self-organizing manner to acquire selectivity for the velocity of an input stimulus. The proposed learning rule would be plausible in the actual nervous system in that it is simple and can be described with only local information.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N6fe759f2906145ccb3e3ed410533df93
29 N7c4cf4f58b0546a7ada2ee5329366813
30 sg:journal.1081741
31 schema:name Self-organization of the velocity selectivity of a directionally selective neural network
32 schema:pagination 401-407
33 schema:productId N04bc4e4c37e145ad97275292a4b42510
34 N2dc6305ca33045d890f0f8e801e741b8
35 N34e230eb9c7e4011b40fc0a9f4b06723
36 N740dd2506a4543a892e5cdd8dad26fdf
37 Ndb617cc4171a4504a9436387e951f2c5
38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018856138
39 https://doi.org/10.1007/bf00201474
40 schema:sdDatePublished 2019-04-11T14:02
41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
42 schema:sdPublisher N7515ceeab8a14f4089975ad160da4799
43 schema:url http://link.springer.com/10.1007/BF00201474
44 sgo:license sg:explorer/license/
45 sgo:sdDataset articles
46 rdf:type schema:ScholarlyArticle
47 N04bc4e4c37e145ad97275292a4b42510 schema:name dimensions_id
48 schema:value pub.1018856138
49 rdf:type schema:PropertyValue
50 N25bd050ba8a74e59a665afb47b804bcb rdf:first sg:person.01204065322.14
51 rdf:rest N9c70d1f8e6fa4bbea74ee3ca0d0a805a
52 N2dc6305ca33045d890f0f8e801e741b8 schema:name readcube_id
53 schema:value 59d7f853a79f7924cc821272ad1505c8142195f63eccd73f38f4b4119af9f5d3
54 rdf:type schema:PropertyValue
55 N34e230eb9c7e4011b40fc0a9f4b06723 schema:name doi
56 schema:value 10.1007/bf00201474
57 rdf:type schema:PropertyValue
58 N462dc2a069e741c783b71991b192a66f rdf:first sg:person.01135752122.35
59 rdf:rest N25bd050ba8a74e59a665afb47b804bcb
60 N56370f05ab684021835dab14ccb3733e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
61 schema:name Motion Perception
62 rdf:type schema:DefinedTerm
63 N6fe759f2906145ccb3e3ed410533df93 schema:volumeNumber 73
64 rdf:type schema:PublicationVolume
65 N740dd2506a4543a892e5cdd8dad26fdf schema:name nlm_unique_id
66 schema:value 7502533
67 rdf:type schema:PropertyValue
68 N7515ceeab8a14f4089975ad160da4799 schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 N7a527cff98eb4e4c81b964af702d15a7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
71 schema:name Models, Neurological
72 rdf:type schema:DefinedTerm
73 N7c4cf4f58b0546a7ada2ee5329366813 schema:issueNumber 5
74 rdf:type schema:PublicationIssue
75 N8021fdfa226c49c6bece7fab6e0fb34f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Humans
77 rdf:type schema:DefinedTerm
78 N84eba39062e1420ab8ec6df1914ab92d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Animals
80 rdf:type schema:DefinedTerm
81 N9c70d1f8e6fa4bbea74ee3ca0d0a805a rdf:first sg:person.01034442456.31
82 rdf:rest rdf:nil
83 Na1aaeadb0dbe431e88b68913fc3be64b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Nerve Net
85 rdf:type schema:DefinedTerm
86 Ndb617cc4171a4504a9436387e951f2c5 schema:name pubmed_id
87 schema:value 7578477
88 rdf:type schema:PropertyValue
89 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
90 schema:name Information and Computing Sciences
91 rdf:type schema:DefinedTerm
92 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
93 schema:name Artificial Intelligence and Image Processing
94 rdf:type schema:DefinedTerm
95 sg:journal.1081741 schema:issn 0340-1200
96 1432-0770
97 schema:name Biological Cybernetics
98 rdf:type schema:Periodical
99 sg:person.01034442456.31 schema:affiliation https://www.grid.ac/institutes/grid.257114.4
100 schema:familyName Nagano
101 schema:givenName Takashi
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034442456.31
103 rdf:type schema:Person
104 sg:person.01135752122.35 schema:affiliation https://www.grid.ac/institutes/grid.257114.4
105 schema:familyName Miura
106 schema:givenName Ken-ichiro
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01135752122.35
108 rdf:type schema:Person
109 sg:person.01204065322.14 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
110 schema:familyName Kurata
111 schema:givenName Koji
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204065322.14
113 rdf:type schema:Person
114 sg:pub.10.1007/bf00224859 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043342679
115 https://doi.org/10.1007/bf00224859
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/bf00274887 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040688784
118 https://doi.org/10.1007/bf00274887
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/bf00337445 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027058937
121 https://doi.org/10.1007/bf00337445
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/0006-8993(76)90313-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045872409
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/0042-6989(87)90118-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027016738
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/0893-6080(90)90045-m schema:sameAs https://app.dimensions.ai/details/publication/pub.1046399255
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1068/p140105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058162695
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/ijcnn.1993.714237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086272575
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1113/jphysiol.1965.sp007638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046300786
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1113/jphysiol.1974.sp010452 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051123025
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1152/jn.1983.49.5.1127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1081999661
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1152/jn.1986.55.6.1308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079508406
140 rdf:type schema:CreativeWork
141 https://doi.org/10.3169/itej1978.33.479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008924379
142 rdf:type schema:CreativeWork
143 https://www.grid.ac/institutes/grid.136593.b schema:alternateName Osaka University
144 schema:name Department of Biological Engineering, Faculty of Engineering Science, Osaka University, 1-1 Machikaneyama-cho, Toyonaka-shi, 560, Osaka, Japan
145 rdf:type schema:Organization
146 https://www.grid.ac/institutes/grid.257114.4 schema:alternateName Hosei University
147 schema:name Department of Industrial and System Engineering, College of Engineering, Hosei University, 3-7-2 Kajino-cho, Koganei-shi, 184, Tokyo, Japan
148 rdf:type schema:Organization
 




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


...