On directed information theory and Granger causality graphs
Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochasti...
Gespeichert in:
| Veröffentlicht in: | Journal of computational neuroscience 2011-02, Vol.30 (1), p.7-16 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 16 |
|---|---|
| container_issue | 1 |
| container_start_page | 7 |
| container_title | Journal of computational neuroscience |
| container_volume | 30 |
| creator | Amblard, Pierre-Olivier Michel, Olivier J. J. |
| description | Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems. |
| doi_str_mv | 10.1007/s10827-010-0231-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_00535905v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2268797141</sourcerecordid><originalsourceid>FETCH-LOGICAL-c502t-a09957ebc004c80209d0f5cace426091f77638d1c7ca7d47c6d2928e40f11dd13</originalsourceid><addsrcrecordid>eNqFkU1LxDAQhoMo7vrxA7xI8SIeqpOkaRI8LaKrsOBFzyEmqdulH2vSivvvTamuIIingcwz70x4EDrBcIkB-FXAIAhPAUMKhOL0YwdNMeM0zQWnu2gKksiUUUwn6CCEFQAIjmEfTQhQSllGpuj6sUls6Z3pnE3Kpmh9rbuybZJu6Vq_SXRjk7nXzavzidF90FXZbZJXr9fLcIT2Cl0Fd_xVD9Hz3e3TzX26eJw_3MwWqWFAulSDlIy7FwOQGQEEpIWCGW1cRnKQuOA8p8Jiw43mNuMmt0QS4TIoMLYW00N0MeYudaXWvqy136hWl-p-tlDDGwCjTAJ7H9jzkV379q13oVN1GYyrKt24tg9KsiyHuJT-SwpGJGS5GMizX-Sq7X0TvxwhHBMzLiOER8j4NgTviu2lGNRgS422VLSlBlvqI86cfgX3L7Wz24lvPREgIxBia3Dws_nv1E_smZ0M</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>851954479</pqid></control><display><type>article</type><title>On directed information theory and Granger causality graphs</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Amblard, Pierre-Olivier ; Michel, Olivier J. J.</creator><creatorcontrib>Amblard, Pierre-Olivier ; Michel, Olivier J. J.</creatorcontrib><description>Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.</description><identifier>ISSN: 0929-5313</identifier><identifier>EISSN: 1573-6873</identifier><identifier>DOI: 10.1007/s10827-010-0231-x</identifier><identifier>PMID: 20333542</identifier><identifier>CODEN: JCNEFR</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Biomedical and Life Sciences ; Biomedicine ; Causality ; Computer Science ; Engineering Sciences ; Entropy ; Human Genetics ; Humans ; Information Theory ; Neurology ; Neurosciences ; Normal Distribution ; Signal and Image Processing ; Stochastic Processes ; Theory of Computation</subject><ispartof>Journal of computational neuroscience, 2011-02, Vol.30 (1), p.7-16</ispartof><rights>Springer Science+Business Media, LLC 2010</rights><rights>Springer Science+Business Media, LLC 2011</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-a09957ebc004c80209d0f5cace426091f77638d1c7ca7d47c6d2928e40f11dd13</citedby><cites>FETCH-LOGICAL-c502t-a09957ebc004c80209d0f5cace426091f77638d1c7ca7d47c6d2928e40f11dd13</cites><orcidid>0000-0002-0890-0383 ; 0000-0002-7695-7728</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10827-010-0231-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10827-010-0231-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20333542$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00535905$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Amblard, Pierre-Olivier</creatorcontrib><creatorcontrib>Michel, Olivier J. J.</creatorcontrib><title>On directed information theory and Granger causality graphs</title><title>Journal of computational neuroscience</title><addtitle>J Comput Neurosci</addtitle><addtitle>J Comput Neurosci</addtitle><description>Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.</description><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Causality</subject><subject>Computer Science</subject><subject>Engineering Sciences</subject><subject>Entropy</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Information Theory</subject><subject>Neurology</subject><subject>Neurosciences</subject><subject>Normal Distribution</subject><subject>Signal and Image Processing</subject><subject>Stochastic Processes</subject><subject>Theory of Computation</subject><issn>0929-5313</issn><issn>1573-6873</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkU1LxDAQhoMo7vrxA7xI8SIeqpOkaRI8LaKrsOBFzyEmqdulH2vSivvvTamuIIingcwz70x4EDrBcIkB-FXAIAhPAUMKhOL0YwdNMeM0zQWnu2gKksiUUUwn6CCEFQAIjmEfTQhQSllGpuj6sUls6Z3pnE3Kpmh9rbuybZJu6Vq_SXRjk7nXzavzidF90FXZbZJXr9fLcIT2Cl0Fd_xVD9Hz3e3TzX26eJw_3MwWqWFAulSDlIy7FwOQGQEEpIWCGW1cRnKQuOA8p8Jiw43mNuMmt0QS4TIoMLYW00N0MeYudaXWvqy136hWl-p-tlDDGwCjTAJ7H9jzkV379q13oVN1GYyrKt24tg9KsiyHuJT-SwpGJGS5GMizX-Sq7X0TvxwhHBMzLiOER8j4NgTviu2lGNRgS422VLSlBlvqI86cfgX3L7Wz24lvPREgIxBia3Dws_nv1E_smZ0M</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Amblard, Pierre-Olivier</creator><creator>Michel, Olivier J. J.</creator><general>Springer US</general><general>Springer Nature B.V</general><general>Springer Verlag</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-0890-0383</orcidid><orcidid>https://orcid.org/0000-0002-7695-7728</orcidid></search><sort><creationdate>20110201</creationdate><title>On directed information theory and Granger causality graphs</title><author>Amblard, Pierre-Olivier ; Michel, Olivier J. J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-a09957ebc004c80209d0f5cace426091f77638d1c7ca7d47c6d2928e40f11dd13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Causality</topic><topic>Computer Science</topic><topic>Engineering Sciences</topic><topic>Entropy</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Information Theory</topic><topic>Neurology</topic><topic>Neurosciences</topic><topic>Normal Distribution</topic><topic>Signal and Image Processing</topic><topic>Stochastic Processes</topic><topic>Theory of Computation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Amblard, Pierre-Olivier</creatorcontrib><creatorcontrib>Michel, Olivier J. J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Journal of computational neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Amblard, Pierre-Olivier</au><au>Michel, Olivier J. J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On directed information theory and Granger causality graphs</atitle><jtitle>Journal of computational neuroscience</jtitle><stitle>J Comput Neurosci</stitle><addtitle>J Comput Neurosci</addtitle><date>2011-02-01</date><risdate>2011</risdate><volume>30</volume><issue>1</issue><spage>7</spage><epage>16</epage><pages>7-16</pages><issn>0929-5313</issn><eissn>1573-6873</eissn><coden>JCNEFR</coden><abstract>Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.</abstract><cop>Boston</cop><pub>Springer US</pub><pmid>20333542</pmid><doi>10.1007/s10827-010-0231-x</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-0890-0383</orcidid><orcidid>https://orcid.org/0000-0002-7695-7728</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0929-5313 |
| ispartof | Journal of computational neuroscience, 2011-02, Vol.30 (1), p.7-16 |
| issn | 0929-5313 1573-6873 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_00535905v1 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Biomedical and Life Sciences Biomedicine Causality Computer Science Engineering Sciences Entropy Human Genetics Humans Information Theory Neurology Neurosciences Normal Distribution Signal and Image Processing Stochastic Processes Theory of Computation |
| title | On directed information theory and Granger causality graphs |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T07%3A02%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=On%20directed%20information%20theory%20and%20Granger%20causality%20graphs&rft.jtitle=Journal%20of%20computational%20neuroscience&rft.au=Amblard,%20Pierre-Olivier&rft.date=2011-02-01&rft.volume=30&rft.issue=1&rft.spage=7&rft.epage=16&rft.pages=7-16&rft.issn=0929-5313&rft.eissn=1573-6873&rft.coden=JCNEFR&rft_id=info:doi/10.1007/s10827-010-0231-x&rft_dat=%3Cproquest_hal_p%3E2268797141%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=851954479&rft_id=info:pmid/20333542&rfr_iscdi=true |