Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1994

AUTHORS

Thomas H. Brown , Sumantra Chattarji

ABSTRACT

SynopsisInterest in the idea that certain forms or aspects of learning and memory result from use-dependent synaptic modifications has a long history [18]. Here we review some of the conceptual and experimental aspects of the type of synaptic learning mechanism originally suggested by the Canadian psychologist Donald Hebb (1949). We first summarize the contemporary concept of a Hebbian synaptic learning mechanism. Theoretical studies suggest that useful and potentially powerful forms of learning and self-organization can emerge in networks of elements that are interconnected by various formal representations of a Hebbian modification [2,49,60,69,70,93]. Interest in the computational aspects of Hebbian modification algorithms has been enhanced by the neurophysiological discovery [11] of a synaptic phenomenon in the hippocampus known as long-term potentiation (LTP), that is induced by a Hebbian mechanism. We review recent facts and hypotheses about LTP that are pertinent to contemporary interpretations of a Hebbian synaptic modification and describe the evolution of biophysical models of spines that account for the key features of LTP induction. Finally we review more recent evidence regarding variations and extensions of Hebb’s original postulate for learning. More... »

PAGES

287-314

Book

TITLE

Models of Neural Networks

ISBN

978-1-4612-8736-0
978-1-4612-4320-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4612-4320-5_8

DOI

http://dx.doi.org/10.1007/978-1-4612-4320-5_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038033115


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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "familyName": "Brown", 
        "givenName": "Thomas H.", 
        "type": "Person"
      }, 
      {
        "familyName": "Chattarji", 
        "givenName": "Sumantra", 
        "id": "sg:person.0601342360.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601342360.20"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1994", 
    "datePublishedReg": "1994-01-01", 
    "description": "SynopsisInterest in the idea that certain forms or aspects of learning and memory result from use-dependent synaptic modifications has a long history [18]. Here we review some of the conceptual and experimental aspects of the type of synaptic learning mechanism originally suggested by the Canadian psychologist Donald Hebb (1949). We first summarize the contemporary concept of a Hebbian synaptic learning mechanism. Theoretical studies suggest that useful and potentially powerful forms of learning and self-organization can emerge in networks of elements that are interconnected by various formal representations of a Hebbian modification [2,49,60,69,70,93]. Interest in the computational aspects of Hebbian modification algorithms has been enhanced by the neurophysiological discovery [11] of a synaptic phenomenon in the hippocampus known as long-term potentiation (LTP), that is induced by a Hebbian mechanism. We review recent facts and hypotheses about LTP that are pertinent to contemporary interpretations of a Hebbian synaptic modification and describe the evolution of biophysical models of spines that account for the key features of LTP induction. Finally we review more recent evidence regarding variations and extensions of Hebb\u2019s original postulate for learning.", 
    "editor": [
      {
        "familyName": "Domany", 
        "givenName": "Eytan", 
        "type": "Person"
      }, 
      {
        "familyName": "van Hemmen", 
        "givenName": "J. Leo", 
        "type": "Person"
      }, 
      {
        "familyName": "Schulten", 
        "givenName": "Klaus", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4612-4320-5_8", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4612-8736-0", 
        "978-1-4612-4320-5"
      ], 
      "name": "Models of Neural Networks", 
      "type": "Book"
    }, 
    "keywords": [
      "idea", 
      "certain forms", 
      "form", 
      "aspects of learning", 
      "aspects", 
      "learning", 
      "memory results", 
      "use-dependent synaptic modifications", 
      "synaptic modification", 
      "long history", 
      "synaptic learning mechanisms", 
      "learning mechanism", 
      "mechanism", 
      "Donald Hebb", 
      "Hebb", 
      "contemporary concepts", 
      "concept", 
      "powerful form", 
      "network of elements", 
      "network", 
      "formal representation", 
      "representation", 
      "Hebbian modification", 
      "interest", 
      "computational aspects", 
      "modification algorithm", 
      "algorithm", 
      "neurophysiological discoveries", 
      "discovery", 
      "synaptic phenomena", 
      "hippocampus", 
      "long-term potentiation", 
      "Hebbian mechanisms", 
      "hypothesis", 
      "contemporary interpretations", 
      "Hebbian synaptic modification", 
      "model", 
      "key features", 
      "features", 
      "LTP induction", 
      "Recent evidence", 
      "evidence", 
      "extension", 
      "original postulate", 
      "postulates", 
      "results", 
      "modification", 
      "history", 
      "experimental aspects", 
      "types", 
      "Canadian psychologist Donald Hebb", 
      "psychologist Donald Hebb", 
      "Hebbian synaptic learning mechanism", 
      "theoretical study", 
      "study", 
      "elements", 
      "Hebbian modification algorithms", 
      "phenomenon", 
      "potentiation", 
      "recent facts", 
      "fact", 
      "interpretation", 
      "evolution", 
      "biophysical model", 
      "spine", 
      "induction", 
      "variation", 
      "Hebb\u2019s original postulate"
    ], 
    "name": "Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept", 
    "pagination": "287-314", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1038033115"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4612-4320-5_8"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4612-4320-5_8", 
      "https://app.dimensions.ai/details/publication/pub.1038033115"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T18:52", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_248.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-1-4612-4320-5_8"
  }
]
 

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.10.1007/978-1-4612-4320-5_8'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4612-4320-5_8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4612-4320-5_8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4612-4320-5_8'


 

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

139 TRIPLES      23 PREDICATES      94 URIs      87 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4612-4320-5_8 schema:about anzsrc-for:11
2 anzsrc-for:1109
3 schema:author N987449057611412ca1877326b8fc6693
4 schema:datePublished 1994
5 schema:datePublishedReg 1994-01-01
6 schema:description SynopsisInterest in the idea that certain forms or aspects of learning and memory result from use-dependent synaptic modifications has a long history [18]. Here we review some of the conceptual and experimental aspects of the type of synaptic learning mechanism originally suggested by the Canadian psychologist Donald Hebb (1949). We first summarize the contemporary concept of a Hebbian synaptic learning mechanism. Theoretical studies suggest that useful and potentially powerful forms of learning and self-organization can emerge in networks of elements that are interconnected by various formal representations of a Hebbian modification [2,49,60,69,70,93]. Interest in the computational aspects of Hebbian modification algorithms has been enhanced by the neurophysiological discovery [11] of a synaptic phenomenon in the hippocampus known as long-term potentiation (LTP), that is induced by a Hebbian mechanism. We review recent facts and hypotheses about LTP that are pertinent to contemporary interpretations of a Hebbian synaptic modification and describe the evolution of biophysical models of spines that account for the key features of LTP induction. Finally we review more recent evidence regarding variations and extensions of Hebb’s original postulate for learning.
7 schema:editor N055edbe336544b7983aefca1043e6c69
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N97e1c8df3c894afdb03004361195e310
12 schema:keywords Canadian psychologist Donald Hebb
13 Donald Hebb
14 Hebb
15 Hebbian mechanisms
16 Hebbian modification
17 Hebbian modification algorithms
18 Hebbian synaptic learning mechanism
19 Hebbian synaptic modification
20 Hebb’s original postulate
21 LTP induction
22 Recent evidence
23 algorithm
24 aspects
25 aspects of learning
26 biophysical model
27 certain forms
28 computational aspects
29 concept
30 contemporary concepts
31 contemporary interpretations
32 discovery
33 elements
34 evidence
35 evolution
36 experimental aspects
37 extension
38 fact
39 features
40 form
41 formal representation
42 hippocampus
43 history
44 hypothesis
45 idea
46 induction
47 interest
48 interpretation
49 key features
50 learning
51 learning mechanism
52 long history
53 long-term potentiation
54 mechanism
55 memory results
56 model
57 modification
58 modification algorithm
59 network
60 network of elements
61 neurophysiological discoveries
62 original postulate
63 phenomenon
64 postulates
65 potentiation
66 powerful form
67 psychologist Donald Hebb
68 recent facts
69 representation
70 results
71 spine
72 study
73 synaptic learning mechanisms
74 synaptic modification
75 synaptic phenomena
76 theoretical study
77 types
78 use-dependent synaptic modifications
79 variation
80 schema:name Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept
81 schema:pagination 287-314
82 schema:productId N435bf4fa31b64522923a332096eaf1a7
83 N5fb5339178fc44a4bad789997f4a495d
84 schema:publisher Nbe25b12bb0d5494aaecf3ac56437951b
85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038033115
86 https://doi.org/10.1007/978-1-4612-4320-5_8
87 schema:sdDatePublished 2021-11-01T18:52
88 schema:sdLicense https://scigraph.springernature.com/explorer/license/
89 schema:sdPublisher Na85eaf5abc8e45aca85aa1e87ce04232
90 schema:url https://doi.org/10.1007/978-1-4612-4320-5_8
91 sgo:license sg:explorer/license/
92 sgo:sdDataset chapters
93 rdf:type schema:Chapter
94 N04dbc2a6e88a40ea9b6ade0045d5228c schema:familyName Domany
95 schema:givenName Eytan
96 rdf:type schema:Person
97 N055edbe336544b7983aefca1043e6c69 rdf:first N04dbc2a6e88a40ea9b6ade0045d5228c
98 rdf:rest N9ff79ad0cd5444d396872d8c9a78e1b9
99 N0d3765d541534702a35f4a6887636a1c schema:familyName van Hemmen
100 schema:givenName J. Leo
101 rdf:type schema:Person
102 N4339e4144ed345f68f720be8aebe210e rdf:first sg:person.0601342360.20
103 rdf:rest rdf:nil
104 N435bf4fa31b64522923a332096eaf1a7 schema:name doi
105 schema:value 10.1007/978-1-4612-4320-5_8
106 rdf:type schema:PropertyValue
107 N5fb5339178fc44a4bad789997f4a495d schema:name dimensions_id
108 schema:value pub.1038033115
109 rdf:type schema:PropertyValue
110 N65b4e01c73b1451aac6b019e336ee555 schema:familyName Brown
111 schema:givenName Thomas H.
112 rdf:type schema:Person
113 N6b38634b6c4c4b3080f5e82cd820fc88 schema:familyName Schulten
114 schema:givenName Klaus
115 rdf:type schema:Person
116 N97e1c8df3c894afdb03004361195e310 schema:isbn 978-1-4612-4320-5
117 978-1-4612-8736-0
118 schema:name Models of Neural Networks
119 rdf:type schema:Book
120 N987449057611412ca1877326b8fc6693 rdf:first N65b4e01c73b1451aac6b019e336ee555
121 rdf:rest N4339e4144ed345f68f720be8aebe210e
122 N9ff79ad0cd5444d396872d8c9a78e1b9 rdf:first N0d3765d541534702a35f4a6887636a1c
123 rdf:rest Nf22a1c02fb4b4634a8c9e84a1c99fa6a
124 Na85eaf5abc8e45aca85aa1e87ce04232 schema:name Springer Nature - SN SciGraph project
125 rdf:type schema:Organization
126 Nbe25b12bb0d5494aaecf3ac56437951b schema:name Springer Nature
127 rdf:type schema:Organisation
128 Nf22a1c02fb4b4634a8c9e84a1c99fa6a rdf:first N6b38634b6c4c4b3080f5e82cd820fc88
129 rdf:rest rdf:nil
130 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
131 schema:name Medical and Health Sciences
132 rdf:type schema:DefinedTerm
133 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
134 schema:name Neurosciences
135 rdf:type schema:DefinedTerm
136 sg:person.0601342360.20 schema:familyName Chattarji
137 schema:givenName Sumantra
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601342360.20
139 rdf:type schema:Person
 




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


...