A hierarchical neural-network model for control and learning of voluntary movement View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1987-10

AUTHORS

M. Kawato, Kazunori Furukawa, R. Suzuki

ABSTRACT

In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS) More... »

PAGES

169-185

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00364149

DOI

http://dx.doi.org/10.1007/bf00364149

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004224027

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/3676355


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Psychological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Motor Activity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Movement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neuronal Plasticity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neurons", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stochastic Processes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Synapses", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Osaka University", 
          "id": "https://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University, 560, Toyonaka, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kawato", 
        "givenName": "M.", 
        "id": "sg:person.01230705277.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230705277.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osaka University", 
          "id": "https://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University, 560, Toyonaka, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Furukawa", 
        "givenName": "Kazunori", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osaka University", 
          "id": "https://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University, 560, Toyonaka, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Suzuki", 
        "givenName": "R.", 
        "id": "sg:person.013656713222.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013656713222.33"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00335202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001979989", 
          "https://doi.org/10.1007/bf00335202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00335202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001979989", 
          "https://doi.org/10.1007/bf00335202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0025-5564(71)90051-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006189062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0006-3495(72)86068-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008475623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00238619", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008768098", 
          "https://doi.org/10.1007/bf00238619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00238619", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008768098", 
          "https://doi.org/10.1007/bf00238619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00336193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013365453", 
          "https://doi.org/10.1007/bf00336193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00336193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013365453", 
          "https://doi.org/10.1007/bf00336193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00365233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013819646", 
          "https://doi.org/10.1007/bf00365233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00365233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013819646", 
          "https://doi.org/10.1007/bf00365233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0166-2236(82)90111-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018540242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0166-2236(82)90111-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018540242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00336192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020281284", 
          "https://doi.org/10.1007/bf00336192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00336192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020281284", 
          "https://doi.org/10.1007/bf00336192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1113/jphysiol.1969.sp008820", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023887875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1113/jphysiol.1982.sp014103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036334567"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00235783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040128031", 
          "https://doi.org/10.1007/bf00235783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00235783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040128031", 
          "https://doi.org/10.1007/bf00235783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00238095", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042548066", 
          "https://doi.org/10.1007/bf00238095"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00238095", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042548066", 
          "https://doi.org/10.1007/bf00238095"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00365229", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045888190", 
          "https://doi.org/10.1007/bf00365229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00365229", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045888190", 
          "https://doi.org/10.1007/bf00365229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-46466-9_25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048982642", 
          "https://doi.org/10.1007/978-3-642-46466-9_25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.ne.04.030181.002031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049461341"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0006-8993(77)90997-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054541774"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0006-8993(77)90997-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054541774"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/proc.1976.10286", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061443393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmc.1980.4308393", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061793226"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3149599", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062103621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3426424", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062121639"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3426922", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062122126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.128123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062471979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0136009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062839801"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.05-07-01688.1985", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1080085642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/physrev.1974.54.4.957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1080302421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1080732212", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1081123147", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.1980.44.4.773", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1081646144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.01-01-00072.1981", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082302093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cdc.1984.272176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086231502"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2183/pjab1945.50.85", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106056798"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1987-10", 
    "datePublishedReg": "1987-10-01", 
    "description": "In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS)", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf00364149", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1081741", 
        "issn": [
          "0340-1200", 
          "1432-0770"
        ], 
        "name": "Biological Cybernetics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "57"
      }
    ], 
    "name": "A hierarchical neural-network model for control and learning of voluntary movement", 
    "pagination": "169-185", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00364149"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1420c3d6511f2c95a0fa69d884b436816859c93e3c64828442a989ce134e70e4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004224027"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7502533"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "3676355"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00364149", 
      "https://app.dimensions.ai/details/publication/pub.1004224027"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T08:51", 
    "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/v90/0000000374_0000000374/records_119737_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF00364149"
  }
]
 

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/bf00364149'

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/bf00364149'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00364149'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00364149'


 

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

230 TRIPLES      21 PREDICATES      72 URIs      33 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00364149 schema:about N1bf9625df4e24f5790952b50a58286de
2 N37167d808ea1464882b8561ecb71c941
3 N3a50b1ed97624dc7b8472c11382d2e0d
4 N5d037826399e4d878c61bd50d80b6695
5 N6e031cb400e44914838a542b9d4f02df
6 N6e5bdc8f80034b049044e0715501d2c4
7 N85a4e790814e42a3b9a2d76b34d5d472
8 N8977c6bb51554f6ebd05e9359d5609f1
9 N9bff9c7e23b249f3946bb3565ca7f41f
10 Nc97c556b0eb9488aba11756691a7538c
11 Ncdf5a8d1899845ba9687a270fcf5c581
12 Nf8badb7bbe2d44ccbe5aba6482974116
13 anzsrc-for:11
14 anzsrc-for:1109
15 schema:author Nadf012f1970247338cd4915f4b128a0c
16 schema:citation sg:pub.10.1007/978-3-642-46466-9_25
17 sg:pub.10.1007/bf00235783
18 sg:pub.10.1007/bf00238095
19 sg:pub.10.1007/bf00238619
20 sg:pub.10.1007/bf00335202
21 sg:pub.10.1007/bf00336192
22 sg:pub.10.1007/bf00336193
23 sg:pub.10.1007/bf00365229
24 sg:pub.10.1007/bf00365233
25 https://app.dimensions.ai/details/publication/pub.1080732212
26 https://app.dimensions.ai/details/publication/pub.1081123147
27 https://doi.org/10.1016/0006-8993(77)90997-0
28 https://doi.org/10.1016/0025-5564(71)90051-4
29 https://doi.org/10.1016/0166-2236(82)90111-4
30 https://doi.org/10.1016/s0006-3495(72)86068-5
31 https://doi.org/10.1109/cdc.1984.272176
32 https://doi.org/10.1109/proc.1976.10286
33 https://doi.org/10.1109/tsmc.1980.4308393
34 https://doi.org/10.1113/jphysiol.1969.sp008820
35 https://doi.org/10.1113/jphysiol.1982.sp014103
36 https://doi.org/10.1115/1.3149599
37 https://doi.org/10.1115/1.3426424
38 https://doi.org/10.1115/1.3426922
39 https://doi.org/10.1126/science.128123
40 https://doi.org/10.1137/0136009
41 https://doi.org/10.1146/annurev.ne.04.030181.002031
42 https://doi.org/10.1152/jn.1980.44.4.773
43 https://doi.org/10.1152/physrev.1974.54.4.957
44 https://doi.org/10.1523/jneurosci.01-01-00072.1981
45 https://doi.org/10.1523/jneurosci.05-07-01688.1985
46 https://doi.org/10.2183/pjab1945.50.85
47 schema:datePublished 1987-10
48 schema:datePublishedReg 1987-10-01
49 schema:description In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS)
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree false
53 schema:isPartOf N22e48bfa9045467e83c96e3ec3ab6f17
54 Nb79fd57fb59a4920bdb3ab7912838a80
55 sg:journal.1081741
56 schema:name A hierarchical neural-network model for control and learning of voluntary movement
57 schema:pagination 169-185
58 schema:productId N06a1da4b84764e7eb69ec2329f530d10
59 N3619bcc9222a4006b7fef43711a1db34
60 N6f1ea8da01384e7cb7408157e361b683
61 N72c22af92aa346259072eb32c9eb160d
62 N9bf64738cdff4a47af40d1cbf57f707d
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004224027
64 https://doi.org/10.1007/bf00364149
65 schema:sdDatePublished 2019-04-15T08:51
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher N3bd864d5ac58438783cf03771f94e43d
68 schema:url http://link.springer.com/10.1007/BF00364149
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N06a1da4b84764e7eb69ec2329f530d10 schema:name pubmed_id
73 schema:value 3676355
74 rdf:type schema:PropertyValue
75 N1bf9625df4e24f5790952b50a58286de schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Brain
77 rdf:type schema:DefinedTerm
78 N22e48bfa9045467e83c96e3ec3ab6f17 schema:issueNumber 3
79 rdf:type schema:PublicationIssue
80 N3619bcc9222a4006b7fef43711a1db34 schema:name readcube_id
81 schema:value 1420c3d6511f2c95a0fa69d884b436816859c93e3c64828442a989ce134e70e4
82 rdf:type schema:PropertyValue
83 N37167d808ea1464882b8561ecb71c941 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Models, Psychological
85 rdf:type schema:DefinedTerm
86 N37880827982046679948622d86ebce11 rdf:first sg:person.013656713222.33
87 rdf:rest rdf:nil
88 N3a50b1ed97624dc7b8472c11382d2e0d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Animals
90 rdf:type schema:DefinedTerm
91 N3bd864d5ac58438783cf03771f94e43d schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 N5d037826399e4d878c61bd50d80b6695 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Motor Activity
95 rdf:type schema:DefinedTerm
96 N6e031cb400e44914838a542b9d4f02df schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Mathematics
98 rdf:type schema:DefinedTerm
99 N6e5bdc8f80034b049044e0715501d2c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Neuronal Plasticity
101 rdf:type schema:DefinedTerm
102 N6f1ea8da01384e7cb7408157e361b683 schema:name doi
103 schema:value 10.1007/bf00364149
104 rdf:type schema:PropertyValue
105 N72c22af92aa346259072eb32c9eb160d schema:name nlm_unique_id
106 schema:value 7502533
107 rdf:type schema:PropertyValue
108 N85a4e790814e42a3b9a2d76b34d5d472 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Learning
110 rdf:type schema:DefinedTerm
111 N8977c6bb51554f6ebd05e9359d5609f1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Movement
113 rdf:type schema:DefinedTerm
114 N9bf64738cdff4a47af40d1cbf57f707d schema:name dimensions_id
115 schema:value pub.1004224027
116 rdf:type schema:PropertyValue
117 N9bff9c7e23b249f3946bb3565ca7f41f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Neurons
119 rdf:type schema:DefinedTerm
120 Nadf012f1970247338cd4915f4b128a0c rdf:first sg:person.01230705277.42
121 rdf:rest Nf5e15cbd7f7d46f1b9531f7fca3be946
122 Nb79fd57fb59a4920bdb3ab7912838a80 schema:volumeNumber 57
123 rdf:type schema:PublicationVolume
124 Nc97c556b0eb9488aba11756691a7538c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Models, Neurological
126 rdf:type schema:DefinedTerm
127 Ncdf5a8d1899845ba9687a270fcf5c581 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Stochastic Processes
129 rdf:type schema:DefinedTerm
130 Nf5e15cbd7f7d46f1b9531f7fca3be946 rdf:first Nfdd403fedde3431fbcb062be09ffaf8c
131 rdf:rest N37880827982046679948622d86ebce11
132 Nf8badb7bbe2d44ccbe5aba6482974116 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Synapses
134 rdf:type schema:DefinedTerm
135 Nfdd403fedde3431fbcb062be09ffaf8c schema:affiliation https://www.grid.ac/institutes/grid.136593.b
136 schema:familyName Furukawa
137 schema:givenName Kazunori
138 rdf:type schema:Person
139 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
140 schema:name Medical and Health Sciences
141 rdf:type schema:DefinedTerm
142 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
143 schema:name Neurosciences
144 rdf:type schema:DefinedTerm
145 sg:journal.1081741 schema:issn 0340-1200
146 1432-0770
147 schema:name Biological Cybernetics
148 rdf:type schema:Periodical
149 sg:person.01230705277.42 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
150 schema:familyName Kawato
151 schema:givenName M.
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230705277.42
153 rdf:type schema:Person
154 sg:person.013656713222.33 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
155 schema:familyName Suzuki
156 schema:givenName R.
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013656713222.33
158 rdf:type schema:Person
159 sg:pub.10.1007/978-3-642-46466-9_25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048982642
160 https://doi.org/10.1007/978-3-642-46466-9_25
161 rdf:type schema:CreativeWork
162 sg:pub.10.1007/bf00235783 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040128031
163 https://doi.org/10.1007/bf00235783
164 rdf:type schema:CreativeWork
165 sg:pub.10.1007/bf00238095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042548066
166 https://doi.org/10.1007/bf00238095
167 rdf:type schema:CreativeWork
168 sg:pub.10.1007/bf00238619 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008768098
169 https://doi.org/10.1007/bf00238619
170 rdf:type schema:CreativeWork
171 sg:pub.10.1007/bf00335202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001979989
172 https://doi.org/10.1007/bf00335202
173 rdf:type schema:CreativeWork
174 sg:pub.10.1007/bf00336192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020281284
175 https://doi.org/10.1007/bf00336192
176 rdf:type schema:CreativeWork
177 sg:pub.10.1007/bf00336193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013365453
178 https://doi.org/10.1007/bf00336193
179 rdf:type schema:CreativeWork
180 sg:pub.10.1007/bf00365229 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045888190
181 https://doi.org/10.1007/bf00365229
182 rdf:type schema:CreativeWork
183 sg:pub.10.1007/bf00365233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013819646
184 https://doi.org/10.1007/bf00365233
185 rdf:type schema:CreativeWork
186 https://app.dimensions.ai/details/publication/pub.1080732212 schema:CreativeWork
187 https://app.dimensions.ai/details/publication/pub.1081123147 schema:CreativeWork
188 https://doi.org/10.1016/0006-8993(77)90997-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054541774
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/0025-5564(71)90051-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006189062
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/0166-2236(82)90111-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018540242
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/s0006-3495(72)86068-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008475623
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1109/cdc.1984.272176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086231502
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1109/proc.1976.10286 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061443393
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1109/tsmc.1980.4308393 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061793226
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1113/jphysiol.1969.sp008820 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023887875
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1113/jphysiol.1982.sp014103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036334567
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1115/1.3149599 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062103621
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1115/1.3426424 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062121639
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1115/1.3426922 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062122126
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1126/science.128123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062471979
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1137/0136009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062839801
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1146/annurev.ne.04.030181.002031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049461341
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1152/jn.1980.44.4.773 schema:sameAs https://app.dimensions.ai/details/publication/pub.1081646144
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1152/physrev.1974.54.4.957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080302421
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1523/jneurosci.01-01-00072.1981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082302093
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1523/jneurosci.05-07-01688.1985 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080085642
225 rdf:type schema:CreativeWork
226 https://doi.org/10.2183/pjab1945.50.85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106056798
227 rdf:type schema:CreativeWork
228 https://www.grid.ac/institutes/grid.136593.b schema:alternateName Osaka University
229 schema:name Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University, 560, Toyonaka, Osaka, Japan
230 rdf:type schema:Organization
 




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


...