Detecting the relevance to performance of whole-body movements View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Daisuke Furuki, Ken Takiyama

ABSTRACT

Goal-directed whole-body movements are fundamental in our daily life, sports, music, art, and other activities. Goal-directed movements have been intensively investigated by focusing on simplified movements (e.g., arm-reaching movements or eye movements); however, the nature of goal-directed whole-body movements has not been sufficiently investigated because of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. One open question is how to overcome high-dimensional nonlinear dynamics and redundancy to achieve the desired performance. It is possible to approach the question by quantifying how the motions of each body part at each time point contribute to movement performance. Nevertheless, it is difficult to identify an explicit relation between each motion element (the motion of each body part at each time point) and performance as a result of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. The current study proposes a data-driven approach to quantify the relevance of each motion element to the performance. The current findings indicate that linear regression may be used to quantify this relevance without considering the high-dimensional nonlinear dynamics of whole-body motion. More... »

PAGES

15659

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-15888-3

DOI

http://dx.doi.org/10.1038/s41598-017-15888-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092621985

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29142276


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tokyo University of Agriculture and Technology", 
          "id": "https://www.grid.ac/institutes/grid.136594.c", 
          "name": [
            "Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, 184-8588, Koganei-shi, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Furuki", 
        "givenName": "Daisuke", 
        "id": "sg:person.010237356207.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010237356207.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo University of Agriculture and Technology", 
          "id": "https://www.grid.ac/institutes/grid.136594.c", 
          "name": [
            "Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, 184-8588, Koganei-shi, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takiyama", 
        "givenName": "Ken", 
        "id": "sg:person.016160077651.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016160077651.07"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/35037588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005827456", 
          "https://doi.org/10.1038/35037588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35037588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005827456", 
          "https://doi.org/10.1038/35037588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/81469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009484325", 
          "https://doi.org/10.1038/81469"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/81469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009484325", 
          "https://doi.org/10.1038/81469"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/089976600300015961", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015123179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00222890009601384", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015432423"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/jocn.1997.9.2.222", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019833887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep23331", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021043141", 
          "https://doi.org/10.1038/srep23331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00221-004-2149-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024465306", 
          "https://doi.org/10.1007/s00221-004-2149-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fncom.2015.00004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027489127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0161324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029504068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/h0055392", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032595070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00353957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036180283", 
          "https://doi.org/10.1007/bf00353957"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms6925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040955177", 
          "https://doi.org/10.1038/ncomms6925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/h0092992", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042724884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2008.02.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047445454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0896-6273(01)00301-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050983820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00401706.1970.10488634", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058284123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.23-27-09032.2003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1076578217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.02-11-01527.1982", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082174034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.14-05-03208.1994", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082690177"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.1996.75.3.1013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082980858"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9780470549148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098662725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9780470549148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098662725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1106875674", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9781118548387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106875674"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-12", 
    "datePublishedReg": "2017-12-01", 
    "description": "Goal-directed whole-body movements are fundamental in our daily life, sports, music, art, and other activities. Goal-directed movements have been intensively investigated by focusing on simplified movements (e.g., arm-reaching movements or eye movements); however, the nature of goal-directed whole-body movements has not been sufficiently investigated because of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. One open question is how to overcome high-dimensional nonlinear dynamics and redundancy to achieve the desired performance. It is possible to approach the question by quantifying how the motions of each body part at each time point contribute to movement performance. Nevertheless, it is difficult to identify an explicit relation between each motion element (the motion of each body part at each time point) and performance as a result of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. The current study proposes a data-driven approach to quantify the relevance of each motion element to the performance. The current findings indicate that linear regression may be used to quantify this relevance without considering the high-dimensional nonlinear dynamics of whole-body motion.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-017-15888-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5921577", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "7"
      }
    ], 
    "name": "Detecting the relevance to performance of whole-body movements", 
    "pagination": "15659", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4cbac30bede7b48e0127673315e70a105c8b7cfc25aa8afda4ca2bfd95901b55"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29142276"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-017-15888-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1092621985"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-017-15888-3", 
      "https://app.dimensions.ai/details/publication/pub.1092621985"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T02:30", 
    "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_00000601.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-017-15888-3"
  }
]
 

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.10.1038/s41598-017-15888-3'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-15888-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-15888-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-15888-3'


 

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

151 TRIPLES      21 PREDICATES      52 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-017-15888-3 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N699675de19c7483d8c1e12f8de59bf33
4 schema:citation sg:pub.10.1007/bf00353957
5 sg:pub.10.1007/s00221-004-2149-x
6 sg:pub.10.1038/35037588
7 sg:pub.10.1038/81469
8 sg:pub.10.1038/ncomms6925
9 sg:pub.10.1038/srep23331
10 https://app.dimensions.ai/details/publication/pub.1106875674
11 https://doi.org/10.1002/9780470549148
12 https://doi.org/10.1002/9781118548387
13 https://doi.org/10.1016/j.neunet.2008.02.003
14 https://doi.org/10.1016/s0896-6273(01)00301-4
15 https://doi.org/10.1037/h0055392
16 https://doi.org/10.1037/h0092992
17 https://doi.org/10.1080/00222890009601384
18 https://doi.org/10.1080/00401706.1970.10488634
19 https://doi.org/10.1152/jn.1996.75.3.1013
20 https://doi.org/10.1162/089976600300015961
21 https://doi.org/10.1162/jocn.1997.9.2.222
22 https://doi.org/10.1371/journal.pone.0161324
23 https://doi.org/10.1523/jneurosci.02-11-01527.1982
24 https://doi.org/10.1523/jneurosci.14-05-03208.1994
25 https://doi.org/10.1523/jneurosci.23-27-09032.2003
26 https://doi.org/10.3389/fncom.2015.00004
27 schema:datePublished 2017-12
28 schema:datePublishedReg 2017-12-01
29 schema:description Goal-directed whole-body movements are fundamental in our daily life, sports, music, art, and other activities. Goal-directed movements have been intensively investigated by focusing on simplified movements (e.g., arm-reaching movements or eye movements); however, the nature of goal-directed whole-body movements has not been sufficiently investigated because of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. One open question is how to overcome high-dimensional nonlinear dynamics and redundancy to achieve the desired performance. It is possible to approach the question by quantifying how the motions of each body part at each time point contribute to movement performance. Nevertheless, it is difficult to identify an explicit relation between each motion element (the motion of each body part at each time point) and performance as a result of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. The current study proposes a data-driven approach to quantify the relevance of each motion element to the performance. The current findings indicate that linear regression may be used to quantify this relevance without considering the high-dimensional nonlinear dynamics of whole-body motion.
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf Na765fea2bee14acdae8910e98305bb7b
34 Nba96d2f2b216468b8ae4c075349f20c2
35 sg:journal.1045337
36 schema:name Detecting the relevance to performance of whole-body movements
37 schema:pagination 15659
38 schema:productId N5093c10433204f7bb8039de63915c6f1
39 N757b2be50c6e495991d39e88cf9e1b36
40 N7cb70397dce64f329b0b20f869d40bb4
41 Naf42a9960ab84694bd69e5fda77d131b
42 Nd3d103092a4846858802c82cbdc0be68
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092621985
44 https://doi.org/10.1038/s41598-017-15888-3
45 schema:sdDatePublished 2019-04-11T02:30
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N3274192229b74943be63d2f91c0528ce
48 schema:url https://www.nature.com/articles/s41598-017-15888-3
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N3274192229b74943be63d2f91c0528ce schema:name Springer Nature - SN SciGraph project
53 rdf:type schema:Organization
54 N5093c10433204f7bb8039de63915c6f1 schema:name doi
55 schema:value 10.1038/s41598-017-15888-3
56 rdf:type schema:PropertyValue
57 N5e21204da9284b748d6b8668903b422b rdf:first sg:person.016160077651.07
58 rdf:rest rdf:nil
59 N699675de19c7483d8c1e12f8de59bf33 rdf:first sg:person.010237356207.31
60 rdf:rest N5e21204da9284b748d6b8668903b422b
61 N757b2be50c6e495991d39e88cf9e1b36 schema:name readcube_id
62 schema:value 4cbac30bede7b48e0127673315e70a105c8b7cfc25aa8afda4ca2bfd95901b55
63 rdf:type schema:PropertyValue
64 N7cb70397dce64f329b0b20f869d40bb4 schema:name pubmed_id
65 schema:value 29142276
66 rdf:type schema:PropertyValue
67 Na765fea2bee14acdae8910e98305bb7b schema:volumeNumber 7
68 rdf:type schema:PublicationVolume
69 Naf42a9960ab84694bd69e5fda77d131b schema:name nlm_unique_id
70 schema:value 101563288
71 rdf:type schema:PropertyValue
72 Nba96d2f2b216468b8ae4c075349f20c2 schema:issueNumber 1
73 rdf:type schema:PublicationIssue
74 Nd3d103092a4846858802c82cbdc0be68 schema:name dimensions_id
75 schema:value pub.1092621985
76 rdf:type schema:PropertyValue
77 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
78 schema:name Information and Computing Sciences
79 rdf:type schema:DefinedTerm
80 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
81 schema:name Artificial Intelligence and Image Processing
82 rdf:type schema:DefinedTerm
83 sg:grant.5921577 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-017-15888-3
84 rdf:type schema:MonetaryGrant
85 sg:journal.1045337 schema:issn 2045-2322
86 schema:name Scientific Reports
87 rdf:type schema:Periodical
88 sg:person.010237356207.31 schema:affiliation https://www.grid.ac/institutes/grid.136594.c
89 schema:familyName Furuki
90 schema:givenName Daisuke
91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010237356207.31
92 rdf:type schema:Person
93 sg:person.016160077651.07 schema:affiliation https://www.grid.ac/institutes/grid.136594.c
94 schema:familyName Takiyama
95 schema:givenName Ken
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016160077651.07
97 rdf:type schema:Person
98 sg:pub.10.1007/bf00353957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036180283
99 https://doi.org/10.1007/bf00353957
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/s00221-004-2149-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024465306
102 https://doi.org/10.1007/s00221-004-2149-x
103 rdf:type schema:CreativeWork
104 sg:pub.10.1038/35037588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005827456
105 https://doi.org/10.1038/35037588
106 rdf:type schema:CreativeWork
107 sg:pub.10.1038/81469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009484325
108 https://doi.org/10.1038/81469
109 rdf:type schema:CreativeWork
110 sg:pub.10.1038/ncomms6925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040955177
111 https://doi.org/10.1038/ncomms6925
112 rdf:type schema:CreativeWork
113 sg:pub.10.1038/srep23331 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021043141
114 https://doi.org/10.1038/srep23331
115 rdf:type schema:CreativeWork
116 https://app.dimensions.ai/details/publication/pub.1106875674 schema:CreativeWork
117 https://doi.org/10.1002/9780470549148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098662725
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1002/9781118548387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106875674
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.neunet.2008.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047445454
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/s0896-6273(01)00301-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050983820
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1037/h0055392 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032595070
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1037/h0092992 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042724884
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1080/00222890009601384 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015432423
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1080/00401706.1970.10488634 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058284123
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1152/jn.1996.75.3.1013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082980858
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1162/089976600300015961 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015123179
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1162/jocn.1997.9.2.222 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019833887
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1371/journal.pone.0161324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029504068
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1523/jneurosci.02-11-01527.1982 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082174034
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1523/jneurosci.14-05-03208.1994 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082690177
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1523/jneurosci.23-27-09032.2003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1076578217
146 rdf:type schema:CreativeWork
147 https://doi.org/10.3389/fncom.2015.00004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027489127
148 rdf:type schema:CreativeWork
149 https://www.grid.ac/institutes/grid.136594.c schema:alternateName Tokyo University of Agriculture and Technology
150 schema:name Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, 184-8588, Koganei-shi, Tokyo, Japan
151 rdf:type schema:Organization
 




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


...