Ontology type: schema:Chapter
2002-08-21
AUTHORS ABSTRACTLong term synaptic changes induced by neural spike activity are believed to underlie learning and memory. Spike-driven long term synaptic plasticity has been investigated in simplified situations in which the patterns of asynchronous activity to be encoded were statistically independent. An extra regulatory mechanism is required to extend the learning capability to more complex and natural stimuli. This mechanism is provided by the effects of the action potentials that are believed to be responsible for spike-timing dependent plasticity. These effects, when combined with the dependence of synaptic plasticity on the post-synaptic depolarization, produce the learning rule needed for storing correlated patterns of asynchronous neuronal activity. More... »
PAGES241-247
Artificial Neural Networks — ICANN 2002
ISBN
978-3-540-44074-1
978-3-540-46084-8
http://scigraph.springernature.com/pub.10.1007/3-540-46084-5_40
DOIhttp://dx.doi.org/10.1007/3-540-46084-5_40
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1024453598
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": [
{
"affiliation": {
"alternateName": "Institute of Physiology, University of Bern, B\u00fchlplatz 5, CH-3012, Switzerland",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Institute of Physiology, University of Bern, B\u00fchlplatz 5, CH-3012, Switzerland"
],
"type": "Organization"
},
"familyName": "Fusi",
"givenName": "Stefano",
"id": "sg:person.01326702501.50",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326702501.50"
],
"type": "Person"
}
],
"datePublished": "2002-08-21",
"datePublishedReg": "2002-08-21",
"description": "Long term synaptic changes induced by neural spike activity are believed to underlie learning and memory. Spike-driven long term synaptic plasticity has been investigated in simplified situations in which the patterns of asynchronous activity to be encoded were statistically independent. An extra regulatory mechanism is required to extend the learning capability to more complex and natural stimuli. This mechanism is provided by the effects of the action potentials that are believed to be responsible for spike-timing dependent plasticity. These effects, when combined with the dependence of synaptic plasticity on the post-synaptic depolarization, produce the learning rule needed for storing correlated patterns of asynchronous neuronal activity.",
"editor": [
{
"familyName": "Dorronsoro",
"givenName": "Jos\u00e9 R.",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/3-540-46084-5_40",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-540-44074-1",
"978-3-540-46084-8"
],
"name": "Artificial Neural Networks \u2014 ICANN 2002",
"type": "Book"
},
"keywords": [
"synaptic plasticity",
"post-synaptic depolarization",
"long-term synaptic plasticity",
"long-term synaptic changes",
"term synaptic plasticity",
"neural spike activity",
"neuronal activity",
"synaptic changes",
"spike activity",
"action potentials",
"asynchronous activity",
"spike-timing dependent plasticity",
"natural stimuli",
"spike-driven synaptic plasticity",
"plasticity",
"regulatory mechanisms",
"activity",
"dependent plasticity",
"depolarization",
"effect",
"patterns",
"stimuli",
"mechanism",
"changes",
"potential",
"memory",
"correlated patterns",
"situation",
"learning rule",
"learning",
"dependence",
"capability",
"rules",
"simplified situation"
],
"name": "Spike- Driven Synaptic Plasticity for Learning Correlated Patterns of Asynchronous Activity",
"pagination": "241-247",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1024453598"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/3-540-46084-5_40"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/3-540-46084-5_40",
"https://app.dimensions.ai/details/publication/pub.1024453598"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-05-10T10:46",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_314.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/3-540-46084-5_40"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/3-540-46084-5_40'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-46084-5_40'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-46084-5_40'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-46084-5_40'
This table displays all metadata directly associated to this object as RDF triples.
94 TRIPLES
23 PREDICATES
59 URIs
52 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/3-540-46084-5_40 | schema:about | anzsrc-for:11 |
2 | ″ | ″ | anzsrc-for:1109 |
3 | ″ | schema:author | N0b1f13adc70540fda266e43ea225caba |
4 | ″ | schema:datePublished | 2002-08-21 |
5 | ″ | schema:datePublishedReg | 2002-08-21 |
6 | ″ | schema:description | Long term synaptic changes induced by neural spike activity are believed to underlie learning and memory. Spike-driven long term synaptic plasticity has been investigated in simplified situations in which the patterns of asynchronous activity to be encoded were statistically independent. An extra regulatory mechanism is required to extend the learning capability to more complex and natural stimuli. This mechanism is provided by the effects of the action potentials that are believed to be responsible for spike-timing dependent plasticity. These effects, when combined with the dependence of synaptic plasticity on the post-synaptic depolarization, produce the learning rule needed for storing correlated patterns of asynchronous neuronal activity. |
7 | ″ | schema:editor | N015564dcebca4e4c8f86e070d0d46718 |
8 | ″ | schema:genre | chapter |
9 | ″ | schema:inLanguage | en |
10 | ″ | schema:isAccessibleForFree | false |
11 | ″ | schema:isPartOf | Nc29061a6b52d41118691361b88a19ea2 |
12 | ″ | schema:keywords | action potentials |
13 | ″ | ″ | activity |
14 | ″ | ″ | asynchronous activity |
15 | ″ | ″ | capability |
16 | ″ | ″ | changes |
17 | ″ | ″ | correlated patterns |
18 | ″ | ″ | dependence |
19 | ″ | ″ | dependent plasticity |
20 | ″ | ″ | depolarization |
21 | ″ | ″ | effect |
22 | ″ | ″ | learning |
23 | ″ | ″ | learning rule |
24 | ″ | ″ | long-term synaptic changes |
25 | ″ | ″ | long-term synaptic plasticity |
26 | ″ | ″ | mechanism |
27 | ″ | ″ | memory |
28 | ″ | ″ | natural stimuli |
29 | ″ | ″ | neural spike activity |
30 | ″ | ″ | neuronal activity |
31 | ″ | ″ | patterns |
32 | ″ | ″ | plasticity |
33 | ″ | ″ | post-synaptic depolarization |
34 | ″ | ″ | potential |
35 | ″ | ″ | regulatory mechanisms |
36 | ″ | ″ | rules |
37 | ″ | ″ | simplified situation |
38 | ″ | ″ | situation |
39 | ″ | ″ | spike activity |
40 | ″ | ″ | spike-driven synaptic plasticity |
41 | ″ | ″ | spike-timing dependent plasticity |
42 | ″ | ″ | stimuli |
43 | ″ | ″ | synaptic changes |
44 | ″ | ″ | synaptic plasticity |
45 | ″ | ″ | term synaptic plasticity |
46 | ″ | schema:name | Spike- Driven Synaptic Plasticity for Learning Correlated Patterns of Asynchronous Activity |
47 | ″ | schema:pagination | 241-247 |
48 | ″ | schema:productId | N247e0f141e7f49f48d481a6eb3de1395 |
49 | ″ | ″ | N521900b3f2ff4b4db4399a7ad0e0c7b8 |
50 | ″ | schema:publisher | Nb6acb4e3633646e2bada23e4b6b249b7 |
51 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1024453598 |
52 | ″ | ″ | https://doi.org/10.1007/3-540-46084-5_40 |
53 | ″ | schema:sdDatePublished | 2022-05-10T10:46 |
54 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
55 | ″ | schema:sdPublisher | Nb56d17bf3a1f4a86b20a6bdc1761fc62 |
56 | ″ | schema:url | https://doi.org/10.1007/3-540-46084-5_40 |
57 | ″ | sgo:license | sg:explorer/license/ |
58 | ″ | sgo:sdDataset | chapters |
59 | ″ | rdf:type | schema:Chapter |
60 | N015564dcebca4e4c8f86e070d0d46718 | rdf:first | Nd73102b8dfb446cd99009fd00a953e6c |
61 | ″ | rdf:rest | rdf:nil |
62 | N0b1f13adc70540fda266e43ea225caba | rdf:first | sg:person.01326702501.50 |
63 | ″ | rdf:rest | rdf:nil |
64 | N247e0f141e7f49f48d481a6eb3de1395 | schema:name | doi |
65 | ″ | schema:value | 10.1007/3-540-46084-5_40 |
66 | ″ | rdf:type | schema:PropertyValue |
67 | N521900b3f2ff4b4db4399a7ad0e0c7b8 | schema:name | dimensions_id |
68 | ″ | schema:value | pub.1024453598 |
69 | ″ | rdf:type | schema:PropertyValue |
70 | Nb56d17bf3a1f4a86b20a6bdc1761fc62 | schema:name | Springer Nature - SN SciGraph project |
71 | ″ | rdf:type | schema:Organization |
72 | Nb6acb4e3633646e2bada23e4b6b249b7 | schema:name | Springer Nature |
73 | ″ | rdf:type | schema:Organisation |
74 | Nc29061a6b52d41118691361b88a19ea2 | schema:isbn | 978-3-540-44074-1 |
75 | ″ | ″ | 978-3-540-46084-8 |
76 | ″ | schema:name | Artificial Neural Networks — ICANN 2002 |
77 | ″ | rdf:type | schema:Book |
78 | Nd73102b8dfb446cd99009fd00a953e6c | schema:familyName | Dorronsoro |
79 | ″ | schema:givenName | José R. |
80 | ″ | rdf:type | schema:Person |
81 | anzsrc-for:11 | schema:inDefinedTermSet | anzsrc-for: |
82 | ″ | schema:name | Medical and Health Sciences |
83 | ″ | rdf:type | schema:DefinedTerm |
84 | anzsrc-for:1109 | schema:inDefinedTermSet | anzsrc-for: |
85 | ″ | schema:name | Neurosciences |
86 | ″ | rdf:type | schema:DefinedTerm |
87 | sg:person.01326702501.50 | schema:affiliation | grid-institutes:None |
88 | ″ | schema:familyName | Fusi |
89 | ″ | schema:givenName | Stefano |
90 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326702501.50 |
91 | ″ | rdf:type | schema:Person |
92 | grid-institutes:None | schema:alternateName | Institute of Physiology, University of Bern, Bühlplatz 5, CH-3012, Switzerland |
93 | ″ | schema:name | Institute of Physiology, University of Bern, Bühlplatz 5, CH-3012, Switzerland |
94 | ″ | rdf:type | schema:Organization |