Ontology type: schema:ScholarlyArticle
1992-09
AUTHORS ABSTRACTChaos theory is a rapidly growing field. As a technical term, "chaos" refers to deterministic but unpredictable processes being sensitively dependent upon initial conditions. Neurobiological models and experimental results are very complicated and some research groups have tried to pursue the "neuronal chaos". Babloyantz's group has studied the fractal dimension (d) of electroencephalograms (EEG) in various physiological and pathological states. From deep sleep (d = 4) to full awakening (d > 8), a hierarchy of "strange" attractors paralles the hierarchy of states of consciousness. In epilepsy (petit mal), despite the turbulent aspect of a seizure, the attractor dimension was near to 2. In Creutzfeld-Jacob disease, the regular EEG activity corresponded to an attractor dimension less than the one measured in deep sleep. Is it healthy to be chaotic? An "active desynchronisation" could be favourable to a physiological system. Rapp's group reported variations of fractal dimension according to particular tasks. During a mental arithmetic task, this dimension increased. In another task, a P300 fractal index decreased when a target was identified. It is clear that the EEG is not representing noise. Its underlying dynamics depends on only a few degrees of freedom despite yet it is difficult to compute accurately the relevant parameters. What is the cognitive role of such a chaotic dynamics? Freeman has studied the olfactory bulb in rabbits and rats for 15 years. Multi-electrode recordings of a few mm2 showed a chaotic hierarchy from deep anaesthesia to alert state. When an animal identified a previously learned odour, the fractal dimension of the dynamics dropped off (near limit cycles).(ABSTRACT TRUNCATED AT 250 WORDS) More... »
PAGES113-119
http://scigraph.springernature.com/pub.10.1007/bf00168140
DOIhttp://dx.doi.org/10.1007/bf00168140
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1042576965
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/1462729
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"
},
{
"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": "Models, Neurological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Nervous System Physiological Phenomena",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Reference Values",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"name": [
"Unit\u00e9 INSERM 230, Service de Neurologie, CHU PURPAN, 31059, TOULOUSE CEDEX, France"
],
"type": "Organization"
},
"familyName": "Doyon",
"givenName": "B.",
"id": "sg:person.01264237031.67",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01264237031.67"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1073/pnas.84.9.2727",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000594280"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(90)90086-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000752823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(90)90086-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000752823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0006-8993(87)90933-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003227710"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0006-8993(87)90933-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003227710"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.86.5.1698",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003412871"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(88)90001-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006102384"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(88)90001-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006102384"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00318715",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006729025",
"https://doi.org/10.1007/bf00318715"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00318715",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006729025",
"https://doi.org/10.1007/bf00318715"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0361-9230(88)90170-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009135998"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0361-9230(88)90170-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009135998"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00317988",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010541621",
"https://doi.org/10.1007/bf00317988"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00317988",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010541621",
"https://doi.org/10.1007/bf00317988"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02458853",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012162785",
"https://doi.org/10.1007/bf02458853"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02458853",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012162785",
"https://doi.org/10.1007/bf02458853"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.83.10.3513",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012598767"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.81.4.1244",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014506983"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00319976",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015601538",
"https://doi.org/10.1007/bf00319976"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00319976",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015601538",
"https://doi.org/10.1007/bf00319976"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/338334a0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017767690",
"https://doi.org/10.1038/338334a0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.86.18.7265",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021057202"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0361-9230(88)90169-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021902474"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0361-9230(88)90169-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021902474"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(90)90050-u",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029831347"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(90)90050-u",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029831347"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0167-2789(85)90011-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030878297"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0167-2789(85)90011-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030878297"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0166-2236(90)90039-d",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032182198"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0166-2236(90)90039-d",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032182198"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047336",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032543255"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047336",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032543255"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047336",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032543255"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0010-0277(89)90006-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033102832"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047403",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035724328"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047403",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035724328"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047403",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035724328"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047452",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037699721"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047452",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037699721"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0140525x00047452",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037699721"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.79.8.2554",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038762424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0167-2789(86)90238-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040090482"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-94-009-0643-3_140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041499764",
"https://doi.org/10.1007/978-94-009-0643-3_140"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00202899",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043727259",
"https://doi.org/10.1007/bf00202899"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00202899",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043727259",
"https://doi.org/10.1007/bf00202899"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0375-9601(85)90444-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047359424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0375-9601(85)90444-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047359424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(89)90018-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050194804"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0893-6080(89)90018-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050194804"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00217660",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051331323",
"https://doi.org/10.1007/bf00217660"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.86.9.2991",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053713321"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.28.2591",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060471424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.28.2591",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060471424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.50.346",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060788721"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.50.346",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060788721"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.61.259",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060797987"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.61.259",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060797987"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tsmc.1983.6313072",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061793451"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tsmc.1983.6313073",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061793452"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1126/science.2916117",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062573463"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1152/ajpregu.1983.245.4.r484",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1081865588"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/ijcnn.1989.118648",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1086195683"
],
"type": "CreativeWork"
}
],
"datePublished": "1992-09",
"datePublishedReg": "1992-09-01",
"description": "Chaos theory is a rapidly growing field. As a technical term, \"chaos\" refers to deterministic but unpredictable processes being sensitively dependent upon initial conditions. Neurobiological models and experimental results are very complicated and some research groups have tried to pursue the \"neuronal chaos\". Babloyantz's group has studied the fractal dimension (d) of electroencephalograms (EEG) in various physiological and pathological states. From deep sleep (d = 4) to full awakening (d > 8), a hierarchy of \"strange\" attractors paralles the hierarchy of states of consciousness. In epilepsy (petit mal), despite the turbulent aspect of a seizure, the attractor dimension was near to 2. In Creutzfeld-Jacob disease, the regular EEG activity corresponded to an attractor dimension less than the one measured in deep sleep. Is it healthy to be chaotic? An \"active desynchronisation\" could be favourable to a physiological system. Rapp's group reported variations of fractal dimension according to particular tasks. During a mental arithmetic task, this dimension increased. In another task, a P300 fractal index decreased when a target was identified. It is clear that the EEG is not representing noise. Its underlying dynamics depends on only a few degrees of freedom despite yet it is difficult to compute accurately the relevant parameters. What is the cognitive role of such a chaotic dynamics? Freeman has studied the olfactory bulb in rabbits and rats for 15 years. Multi-electrode recordings of a few mm2 showed a chaotic hierarchy from deep anaesthesia to alert state. When an animal identified a previously learned odour, the fractal dimension of the dynamics dropped off (near limit cycles).(ABSTRACT TRUNCATED AT 250 WORDS)",
"genre": "research_article",
"id": "sg:pub.10.1007/bf00168140",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1019912",
"issn": [
"0001-5342",
"1572-8358"
],
"name": "Acta Biotheoretica",
"type": "Periodical"
},
{
"issueNumber": "2-3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "40"
}
],
"name": "On the existence and the role of chaotic processes in the nervous system",
"pagination": "113-119",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"cacfeb12c2ee353272cc29a71c1de4bef8a9692433a91dd0332d9dd61b65b3c6"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"1462729"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"0421520"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/bf00168140"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1042576965"
]
}
],
"sameAs": [
"https://doi.org/10.1007/bf00168140",
"https://app.dimensions.ai/details/publication/pub.1042576965"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T14:00",
"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/v90/0000000371_0000000371/records_130829_00000003.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007/BF00168140"
}
]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/bf00168140'
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/bf00168140'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00168140'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00168140'
This table displays all metadata directly associated to this object as RDF triples.
210 TRIPLES
21 PREDICATES
72 URIs
26 LITERALS
14 BLANK NODES