Collective Dynamics in Neural Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Antonio Politi

ABSTRACT

A wealth of natural and artificial systems are composed of many coupled subunits: examples are electric, metabolic, and neural networks that are encountered in engineering and biological contexts, but also the granular media and fluids studied in physics. In such cases, it is natural to expect substantial changes induced by the mutual coupling and it is customary to qualify the overall behaviour as collective. There exists, however, a deeper notion of the term collective, that is related to the concept of thermodynamic phase within equilibrium statistical mechanics. The same set of microscopic equations may generate and sustain different macroscopic states (see, e.g., the gas, liquid, and solid phases), that may be selected by varying a suitable control parameter (e.g., the temperature, or an external field). At equilibrium, the macroscopic phases are necessarily stationary (time-independent) but, out-of-equilibrium, detailed balance is violated, currents are generated and macroscopic oscillations may appear as well. The emergence of collective motion is particularly relevant in the context of neural networks, where its properties are likely to be connected to information processing in a way still to be understood. More... »

PAGES

21-25

References to SciGraph publications

Book

TITLE

ISCS 2014: Interdisciplinary Symposium on Complex Systems

ISBN

978-3-319-10758-5
978-3-319-10759-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10759-2_3

DOI

http://dx.doi.org/10.1007/978-3-319-10759-2_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007007444


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/2008/0101", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Pure Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Aberdeen", 
          "id": "https://www.grid.ac/institutes/grid.7107.1", 
          "name": [
            "Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Politi", 
        "givenName": "Antonio", 
        "id": "sg:person.013264105675.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013264105675.78"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0167-2789(94)90214-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003161251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-2789(94)90214-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003161251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.109.138103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003999713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.109.138103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003999713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-69689-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004761286", 
          "https://doi.org/10.1007/978-3-642-69689-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-69689-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004761286", 
          "https://doi.org/10.1007/978-3-642-69689-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/neco.1995.7.2.307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013057819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-5193(67)90051-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016978064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.81.4116", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024127226"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.81.4116", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024127226"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.105.158104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028004819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.105.158104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028004819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0305-4470/39/26/l01", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043475073"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.93.174102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052048398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.93.174102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052048398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.48.1483", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060715592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.48.1483", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060715592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.54.5522", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060720054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.54.5522", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060720054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.98.064101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060833537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.98.064101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060833537"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015", 
    "datePublishedReg": "2015-01-01", 
    "description": "A wealth of natural and artificial systems are composed of many coupled subunits: examples are electric, metabolic, and neural networks that are encountered in engineering and biological contexts, but also the granular media and fluids studied in physics. In such cases, it is natural to expect substantial changes induced by the mutual coupling and it is customary to qualify the overall behaviour as collective. There exists, however, a deeper notion of the term collective, that is related to the concept of thermodynamic phase within equilibrium statistical mechanics. The same set of microscopic equations may generate and sustain different macroscopic states (see, e.g., the gas, liquid, and solid phases), that may be selected by varying a suitable control parameter (e.g., the temperature, or an external field). At equilibrium, the macroscopic phases are necessarily stationary (time-independent) but, out-of-equilibrium, detailed balance is violated, currents are generated and macroscopic oscillations may appear as well. The emergence of collective motion is particularly relevant in the context of neural networks, where its properties are likely to be connected to information processing in a way still to be understood.", 
    "editor": [
      {
        "familyName": "Sanayei", 
        "givenName": "Ali", 
        "type": "Person"
      }, 
      {
        "familyName": "E. R\u00f6ssler", 
        "givenName": "Otto", 
        "type": "Person"
      }, 
      {
        "familyName": "Zelinka", 
        "givenName": "Ivan", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-10759-2_3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-10758-5", 
        "978-3-319-10759-2"
      ], 
      "name": "ISCS 2014: Interdisciplinary Symposium on Complex Systems", 
      "type": "Book"
    }, 
    "name": "Collective Dynamics in Neural Networks", 
    "pagination": "21-25", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-10759-2_3"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "21616a5090bdd0710a91115dd4415edcc623fde668401f47cad637de423e9134"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1007007444"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-10759-2_3", 
      "https://app.dimensions.ai/details/publication/pub.1007007444"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T00:26", 
    "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_8697_00000558.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-10759-2_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.10.1007/978-3-319-10759-2_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.10.1007/978-3-319-10759-2_3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10759-2_3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10759-2_3'


 

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

112 TRIPLES      23 PREDICATES      39 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-10759-2_3 schema:about anzsrc-for:01
2 anzsrc-for:0101
3 schema:author N2efe3e3bee1d4e85ac3c74343b80ebd6
4 schema:citation sg:pub.10.1007/978-3-642-69689-3
5 https://doi.org/10.1016/0022-5193(67)90051-3
6 https://doi.org/10.1016/0167-2789(94)90214-3
7 https://doi.org/10.1088/0305-4470/39/26/l01
8 https://doi.org/10.1103/physreve.48.1483
9 https://doi.org/10.1103/physreve.54.5522
10 https://doi.org/10.1103/physrevlett.105.158104
11 https://doi.org/10.1103/physrevlett.109.138103
12 https://doi.org/10.1103/physrevlett.81.4116
13 https://doi.org/10.1103/physrevlett.93.174102
14 https://doi.org/10.1103/physrevlett.98.064101
15 https://doi.org/10.1162/neco.1995.7.2.307
16 schema:datePublished 2015
17 schema:datePublishedReg 2015-01-01
18 schema:description A wealth of natural and artificial systems are composed of many coupled subunits: examples are electric, metabolic, and neural networks that are encountered in engineering and biological contexts, but also the granular media and fluids studied in physics. In such cases, it is natural to expect substantial changes induced by the mutual coupling and it is customary to qualify the overall behaviour as collective. There exists, however, a deeper notion of the term collective, that is related to the concept of thermodynamic phase within equilibrium statistical mechanics. The same set of microscopic equations may generate and sustain different macroscopic states (see, e.g., the gas, liquid, and solid phases), that may be selected by varying a suitable control parameter (e.g., the temperature, or an external field). At equilibrium, the macroscopic phases are necessarily stationary (time-independent) but, out-of-equilibrium, detailed balance is violated, currents are generated and macroscopic oscillations may appear as well. The emergence of collective motion is particularly relevant in the context of neural networks, where its properties are likely to be connected to information processing in a way still to be understood.
19 schema:editor N14cd213465e34c629d73d59cdc279748
20 schema:genre chapter
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf Nf8ddba78dd134e66874c18350eae8853
24 schema:name Collective Dynamics in Neural Networks
25 schema:pagination 21-25
26 schema:productId N8934e55e8e3a4af585f67f8ba233dc83
27 Nced3568b18194ddd836143fdef86ae84
28 Nf685d76a16b6448cb00b9e69fbaed140
29 schema:publisher Nc7b4723f42774a11b6d724e3ea46702e
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007007444
31 https://doi.org/10.1007/978-3-319-10759-2_3
32 schema:sdDatePublished 2019-04-16T00:26
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher N7f9d4b8e51b14202830e24187f7f34d2
35 schema:url http://link.springer.com/10.1007/978-3-319-10759-2_3
36 sgo:license sg:explorer/license/
37 sgo:sdDataset chapters
38 rdf:type schema:Chapter
39 N08e1bec679484cdaa3c6f9825eedf27d schema:familyName Zelinka
40 schema:givenName Ivan
41 rdf:type schema:Person
42 N14cd213465e34c629d73d59cdc279748 rdf:first Nba1720fae033429fadda335961f9e539
43 rdf:rest Naea23030bd9644d78e02df2a4ab07517
44 N2efe3e3bee1d4e85ac3c74343b80ebd6 rdf:first sg:person.013264105675.78
45 rdf:rest rdf:nil
46 N733697a001a9426d8401087f8dbfc735 schema:familyName E. Rössler
47 schema:givenName Otto
48 rdf:type schema:Person
49 N7f9d4b8e51b14202830e24187f7f34d2 schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 N85fc2facb7f949bc91c4d38580c34634 rdf:first N08e1bec679484cdaa3c6f9825eedf27d
52 rdf:rest rdf:nil
53 N8934e55e8e3a4af585f67f8ba233dc83 schema:name dimensions_id
54 schema:value pub.1007007444
55 rdf:type schema:PropertyValue
56 Naea23030bd9644d78e02df2a4ab07517 rdf:first N733697a001a9426d8401087f8dbfc735
57 rdf:rest N85fc2facb7f949bc91c4d38580c34634
58 Nba1720fae033429fadda335961f9e539 schema:familyName Sanayei
59 schema:givenName Ali
60 rdf:type schema:Person
61 Nc7b4723f42774a11b6d724e3ea46702e schema:location Cham
62 schema:name Springer International Publishing
63 rdf:type schema:Organisation
64 Nced3568b18194ddd836143fdef86ae84 schema:name doi
65 schema:value 10.1007/978-3-319-10759-2_3
66 rdf:type schema:PropertyValue
67 Nf685d76a16b6448cb00b9e69fbaed140 schema:name readcube_id
68 schema:value 21616a5090bdd0710a91115dd4415edcc623fde668401f47cad637de423e9134
69 rdf:type schema:PropertyValue
70 Nf8ddba78dd134e66874c18350eae8853 schema:isbn 978-3-319-10758-5
71 978-3-319-10759-2
72 schema:name ISCS 2014: Interdisciplinary Symposium on Complex Systems
73 rdf:type schema:Book
74 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
75 schema:name Mathematical Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0101 schema:inDefinedTermSet anzsrc-for:
78 schema:name Pure Mathematics
79 rdf:type schema:DefinedTerm
80 sg:person.013264105675.78 schema:affiliation https://www.grid.ac/institutes/grid.7107.1
81 schema:familyName Politi
82 schema:givenName Antonio
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013264105675.78
84 rdf:type schema:Person
85 sg:pub.10.1007/978-3-642-69689-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004761286
86 https://doi.org/10.1007/978-3-642-69689-3
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1016/0022-5193(67)90051-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016978064
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1016/0167-2789(94)90214-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003161251
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1088/0305-4470/39/26/l01 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043475073
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1103/physreve.48.1483 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060715592
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1103/physreve.54.5522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060720054
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1103/physrevlett.105.158104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028004819
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1103/physrevlett.109.138103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003999713
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1103/physrevlett.81.4116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024127226
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1103/physrevlett.93.174102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052048398
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1103/physrevlett.98.064101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060833537
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1162/neco.1995.7.2.307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013057819
109 rdf:type schema:CreativeWork
110 https://www.grid.ac/institutes/grid.7107.1 schema:alternateName University of Aberdeen
111 schema:name Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
112 rdf:type schema:Organization
 




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


...