Limits on the memory storage capacity of bounded synapses View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-03-11

AUTHORS

Stefano Fusi, L F Abbott

ABSTRACT

Memories maintained in patterns of synaptic connectivity are rapidly overwritten and destroyed by ongoing plasticity related to the storage of new memories. Short memory lifetimes arise from the bounds that must be imposed on synaptic efficacy in any realistic model. We explored whether memory performance can be improved by allowing synapses to traverse a large number of states before reaching their bounds, or by changing the way these bounds are imposed. In the case of hard bounds, memory lifetimes grow proportional to the square of the number of synaptic states, but only if potentiation and depression are precisely balanced. Improved performance can be obtained without fine tuning by imposing soft bounds, but this improvement is only linear with respect to the number of synaptic states. We explored several other possibilities and conclude that improving memory performance requires a more radical modification of the standard model of memory storage. More... »

PAGES

485-493

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nn1859

DOI

http://dx.doi.org/10.1038/nn1859

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027082096

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17351638


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/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "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": "Memory", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neuronal Plasticity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Synapses", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA", 
          "id": "http://www.grid.ac/institutes/grid.21729.3f", 
          "name": [
            "Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA"
          ], 
          "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"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA", 
          "id": "http://www.grid.ac/institutes/grid.21729.3f", 
          "name": [
            "Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abbott", 
        "givenName": "L F", 
        "id": "sg:person.015576001142.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015576001142.27"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.3758/bf03211316", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043596486", 
          "https://doi.org/10.3758/bf03211316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn776", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000942452", 
          "https://doi.org/10.1038/nn776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/29783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025613455", 
          "https://doi.org/10.1038/29783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/39892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030782692", 
          "https://doi.org/10.1038/39892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/222960a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006268665", 
          "https://doi.org/10.1038/222960a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00422-002-0356-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040028046", 
          "https://doi.org/10.1007/s00422-002-0356-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008512477", 
          "https://doi.org/10.1038/nature03012"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/361031a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042608618", 
          "https://doi.org/10.1038/361031a0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-03-11", 
    "datePublishedReg": "2007-03-11", 
    "description": "Memories maintained in patterns of synaptic connectivity are rapidly overwritten and destroyed by ongoing plasticity related to the storage of new memories. Short memory lifetimes arise from the bounds that must be imposed on synaptic efficacy in any realistic model. We explored whether memory performance can be improved by allowing synapses to traverse a large number of states before reaching their bounds, or by changing the way these bounds are imposed. In the case of hard bounds, memory lifetimes grow proportional to the square of the number of synaptic states, but only if potentiation and depression are precisely balanced. Improved performance can be obtained without fine tuning by imposing soft bounds, but this improvement is only linear with respect to the number of synaptic states. We explored several other possibilities and conclude that improving memory performance requires a more radical modification of the standard model of memory storage.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/nn1859", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1118362", 
        "issn": [
          "1097-6256", 
          "1546-1726"
        ], 
        "name": "Nature Neuroscience", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "keywords": [
      "synaptic connectivity", 
      "synaptic efficacy", 
      "ongoing plasticity", 
      "memory performance", 
      "synaptic states", 
      "synapses", 
      "new memories", 
      "potentiation", 
      "efficacy", 
      "depression", 
      "number", 
      "plasticity", 
      "cases", 
      "memory storage", 
      "memory", 
      "large number", 
      "improvement", 
      "patterns", 
      "model", 
      "radical modification", 
      "connectivity", 
      "state", 
      "possibility", 
      "modification", 
      "capacity", 
      "fine tuning", 
      "memory storage capacity", 
      "respect", 
      "storage", 
      "performance", 
      "short memory", 
      "way", 
      "limit", 
      "improved performance", 
      "realistic model", 
      "squares", 
      "lifetime", 
      "tuning", 
      "memory lifetime", 
      "soft bounds", 
      "storage capacity", 
      "standard model", 
      "bounds", 
      "hard bounds"
    ], 
    "name": "Limits on the memory storage capacity of bounded synapses", 
    "pagination": "485-493", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027082096"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/nn1859"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17351638"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/nn1859", 
      "https://app.dimensions.ai/details/publication/pub.1027082096"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_434.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/nn1859"
  }
]
 

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/nn1859'

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/nn1859'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nn1859'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nn1859'


 

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

169 TRIPLES      22 PREDICATES      84 URIs      68 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/nn1859 schema:about N283c12f18a4e4c348d24d707d781d25e
2 N4879e198d0c5421e984f97ce0807fad2
3 Na86da1eb91e94ed2b6ab54b481bf29dd
4 Naa73cca8ee6c435795e4a4d499d78eac
5 Nd990c6ad7b244253b89859c5d3e22b4a
6 Ne50d5d4af9034e238f17baef751ed2e5
7 anzsrc-for:17
8 anzsrc-for:1701
9 schema:author N5df4e382c13f479fbaece09afa84792c
10 schema:citation sg:pub.10.1007/s00422-002-0356-8
11 sg:pub.10.1038/222960a0
12 sg:pub.10.1038/29783
13 sg:pub.10.1038/361031a0
14 sg:pub.10.1038/39892
15 sg:pub.10.1038/nature03012
16 sg:pub.10.1038/nn776
17 sg:pub.10.3758/bf03211316
18 schema:datePublished 2007-03-11
19 schema:datePublishedReg 2007-03-11
20 schema:description Memories maintained in patterns of synaptic connectivity are rapidly overwritten and destroyed by ongoing plasticity related to the storage of new memories. Short memory lifetimes arise from the bounds that must be imposed on synaptic efficacy in any realistic model. We explored whether memory performance can be improved by allowing synapses to traverse a large number of states before reaching their bounds, or by changing the way these bounds are imposed. In the case of hard bounds, memory lifetimes grow proportional to the square of the number of synaptic states, but only if potentiation and depression are precisely balanced. Improved performance can be obtained without fine tuning by imposing soft bounds, but this improvement is only linear with respect to the number of synaptic states. We explored several other possibilities and conclude that improving memory performance requires a more radical modification of the standard model of memory storage.
21 schema:genre article
22 schema:inLanguage en
23 schema:isAccessibleForFree false
24 schema:isPartOf N6eccf754f11a4cc6b9ad339527b05558
25 N7cc6869c833648b6b9b429cc7c408b7f
26 sg:journal.1118362
27 schema:keywords bounds
28 capacity
29 cases
30 connectivity
31 depression
32 efficacy
33 fine tuning
34 hard bounds
35 improved performance
36 improvement
37 large number
38 lifetime
39 limit
40 memory
41 memory lifetime
42 memory performance
43 memory storage
44 memory storage capacity
45 model
46 modification
47 new memories
48 number
49 ongoing plasticity
50 patterns
51 performance
52 plasticity
53 possibility
54 potentiation
55 radical modification
56 realistic model
57 respect
58 short memory
59 soft bounds
60 squares
61 standard model
62 state
63 storage
64 storage capacity
65 synapses
66 synaptic connectivity
67 synaptic efficacy
68 synaptic states
69 tuning
70 way
71 schema:name Limits on the memory storage capacity of bounded synapses
72 schema:pagination 485-493
73 schema:productId N11a9e789143b4dca991fac58a7b826fb
74 N49e12a3134ce489786a2f75fb5b74e44
75 N9c35ec60075e423aa50051727401d980
76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027082096
77 https://doi.org/10.1038/nn1859
78 schema:sdDatePublished 2022-05-20T07:23
79 schema:sdLicense https://scigraph.springernature.com/explorer/license/
80 schema:sdPublisher Nfac77fb5c10a486689c913fef3c9776e
81 schema:url https://doi.org/10.1038/nn1859
82 sgo:license sg:explorer/license/
83 sgo:sdDataset articles
84 rdf:type schema:ScholarlyArticle
85 N11a9e789143b4dca991fac58a7b826fb schema:name pubmed_id
86 schema:value 17351638
87 rdf:type schema:PropertyValue
88 N283c12f18a4e4c348d24d707d781d25e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Humans
90 rdf:type schema:DefinedTerm
91 N4879e198d0c5421e984f97ce0807fad2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Models, Neurological
93 rdf:type schema:DefinedTerm
94 N49e12a3134ce489786a2f75fb5b74e44 schema:name doi
95 schema:value 10.1038/nn1859
96 rdf:type schema:PropertyValue
97 N5df4e382c13f479fbaece09afa84792c rdf:first sg:person.01326702501.50
98 rdf:rest Na08f3be9e62e45bfab2062a294eaf9be
99 N6eccf754f11a4cc6b9ad339527b05558 schema:issueNumber 4
100 rdf:type schema:PublicationIssue
101 N7cc6869c833648b6b9b429cc7c408b7f schema:volumeNumber 10
102 rdf:type schema:PublicationVolume
103 N9c35ec60075e423aa50051727401d980 schema:name dimensions_id
104 schema:value pub.1027082096
105 rdf:type schema:PropertyValue
106 Na08f3be9e62e45bfab2062a294eaf9be rdf:first sg:person.015576001142.27
107 rdf:rest rdf:nil
108 Na86da1eb91e94ed2b6ab54b481bf29dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Neuronal Plasticity
110 rdf:type schema:DefinedTerm
111 Naa73cca8ee6c435795e4a4d499d78eac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Animals
113 rdf:type schema:DefinedTerm
114 Nd990c6ad7b244253b89859c5d3e22b4a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Synapses
116 rdf:type schema:DefinedTerm
117 Ne50d5d4af9034e238f17baef751ed2e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Memory
119 rdf:type schema:DefinedTerm
120 Nfac77fb5c10a486689c913fef3c9776e schema:name Springer Nature - SN SciGraph project
121 rdf:type schema:Organization
122 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
123 schema:name Psychology and Cognitive Sciences
124 rdf:type schema:DefinedTerm
125 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
126 schema:name Psychology
127 rdf:type schema:DefinedTerm
128 sg:journal.1118362 schema:issn 1097-6256
129 1546-1726
130 schema:name Nature Neuroscience
131 schema:publisher Springer Nature
132 rdf:type schema:Periodical
133 sg:person.01326702501.50 schema:affiliation grid-institutes:grid.21729.3f
134 schema:familyName Fusi
135 schema:givenName Stefano
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326702501.50
137 rdf:type schema:Person
138 sg:person.015576001142.27 schema:affiliation grid-institutes:grid.21729.3f
139 schema:familyName Abbott
140 schema:givenName L F
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015576001142.27
142 rdf:type schema:Person
143 sg:pub.10.1007/s00422-002-0356-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040028046
144 https://doi.org/10.1007/s00422-002-0356-8
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/222960a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006268665
147 https://doi.org/10.1038/222960a0
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/29783 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025613455
150 https://doi.org/10.1038/29783
151 rdf:type schema:CreativeWork
152 sg:pub.10.1038/361031a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042608618
153 https://doi.org/10.1038/361031a0
154 rdf:type schema:CreativeWork
155 sg:pub.10.1038/39892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030782692
156 https://doi.org/10.1038/39892
157 rdf:type schema:CreativeWork
158 sg:pub.10.1038/nature03012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008512477
159 https://doi.org/10.1038/nature03012
160 rdf:type schema:CreativeWork
161 sg:pub.10.1038/nn776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000942452
162 https://doi.org/10.1038/nn776
163 rdf:type schema:CreativeWork
164 sg:pub.10.3758/bf03211316 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043596486
165 https://doi.org/10.3758/bf03211316
166 rdf:type schema:CreativeWork
167 grid-institutes:grid.21729.3f schema:alternateName Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA
168 schema:name Center for Neurobiology and Behavior, Kolb Research Annex, Columbia University College of Physicians and Surgeons, 1051 Riverside Drive, 10032-2695, New York, New York, USA
169 rdf:type schema:Organization
 




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


...