Building population models for large-scale neural recordings: Opportunities and pitfalls
Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and co...
Gespeichert in:
| Veröffentlicht in: | Current opinion in neurobiology 2021-10, Vol.70, p.64-73 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 73 |
|---|---|
| container_issue | |
| container_start_page | 64 |
| container_title | Current opinion in neurobiology |
| container_volume | 70 |
| creator | Hurwitz, Cole Kudryashova, Nina Onken, Arno Hennig, Matthias H. |
| description | Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide. |
| doi_str_mv | 10.1016/j.conb.2021.07.003 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2563427721</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0959438821000726</els_id><sourcerecordid>2563427721</sourcerecordid><originalsourceid>FETCH-LOGICAL-c356t-2e19c04a5a59f3f39de59a637f61dfb6ea1e30e4d48702883ba15db807a92dd43</originalsourceid><addsrcrecordid>eNp9kLFu2zAURYkiReO4_YEOBccsUh5JSaSCLInRtAEMeGmBbgRFPhk0aFEhpQL9-8hwmjHTXc49wyHkK4OSAWtuDqWNQ1dy4KwEWQKID2TFlBRFoxS_ICto67aohFKX5CrnAwA0QolP5FJUFWMtyBX58zD74Pywp2Mc52AmHwd6jA5Dpn1MNJi0xyJbE5AOOCcTaEIb0-mSb-luHGOa5sFPHjM1g6Ojn3oTQv5MPi6b8cvrrsnvx--_Nj-L7e7H0-Z-W1hRN1PBkbUWKlObuu1FL1qHdWsaIfuGub5r0DAUgJWrlASulOgMq12nQJqWO1eJNbk-e8cUn2fMkz76bDEEM2Ccs-Z1IyouJWcLys-oTTHnhL0ekz-a9E8z0Kei-qBPRfWpqAapl6LL6durf-6O6N4u_xMuwN0ZWJLhX49JZ-txsOj8UmrSLvr3_C-e_oiG</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2563427721</pqid></control><display><type>article</type><title>Building population models for large-scale neural recordings: Opportunities and pitfalls</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals Complete</source><creator>Hurwitz, Cole ; Kudryashova, Nina ; Onken, Arno ; Hennig, Matthias H.</creator><creatorcontrib>Hurwitz, Cole ; Kudryashova, Nina ; Onken, Arno ; Hennig, Matthias H.</creatorcontrib><description>Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.</description><identifier>ISSN: 0959-4388</identifier><identifier>EISSN: 1873-6882</identifier><identifier>DOI: 10.1016/j.conb.2021.07.003</identifier><identifier>PMID: 34411907</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Neurons - physiology</subject><ispartof>Current opinion in neurobiology, 2021-10, Vol.70, p.64-73</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright © 2021 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-2e19c04a5a59f3f39de59a637f61dfb6ea1e30e4d48702883ba15db807a92dd43</citedby><cites>FETCH-LOGICAL-c356t-2e19c04a5a59f3f39de59a637f61dfb6ea1e30e4d48702883ba15db807a92dd43</cites><orcidid>0000-0001-7387-5535</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.conb.2021.07.003$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34411907$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hurwitz, Cole</creatorcontrib><creatorcontrib>Kudryashova, Nina</creatorcontrib><creatorcontrib>Onken, Arno</creatorcontrib><creatorcontrib>Hennig, Matthias H.</creatorcontrib><title>Building population models for large-scale neural recordings: Opportunities and pitfalls</title><title>Current opinion in neurobiology</title><addtitle>Curr Opin Neurobiol</addtitle><description>Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.</description><subject>Neurons - physiology</subject><issn>0959-4388</issn><issn>1873-6882</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kLFu2zAURYkiReO4_YEOBccsUh5JSaSCLInRtAEMeGmBbgRFPhk0aFEhpQL9-8hwmjHTXc49wyHkK4OSAWtuDqWNQ1dy4KwEWQKID2TFlBRFoxS_ICto67aohFKX5CrnAwA0QolP5FJUFWMtyBX58zD74Pywp2Mc52AmHwd6jA5Dpn1MNJi0xyJbE5AOOCcTaEIb0-mSb-luHGOa5sFPHjM1g6Ojn3oTQv5MPi6b8cvrrsnvx--_Nj-L7e7H0-Z-W1hRN1PBkbUWKlObuu1FL1qHdWsaIfuGub5r0DAUgJWrlASulOgMq12nQJqWO1eJNbk-e8cUn2fMkz76bDEEM2Ccs-Z1IyouJWcLys-oTTHnhL0ekz-a9E8z0Kei-qBPRfWpqAapl6LL6durf-6O6N4u_xMuwN0ZWJLhX49JZ-txsOj8UmrSLvr3_C-e_oiG</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Hurwitz, Cole</creator><creator>Kudryashova, Nina</creator><creator>Onken, Arno</creator><creator>Hennig, Matthias H.</creator><general>Elsevier Ltd</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>7X8</scope><orcidid>https://orcid.org/0000-0001-7387-5535</orcidid></search><sort><creationdate>202110</creationdate><title>Building population models for large-scale neural recordings: Opportunities and pitfalls</title><author>Hurwitz, Cole ; Kudryashova, Nina ; Onken, Arno ; Hennig, Matthias H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-2e19c04a5a59f3f39de59a637f61dfb6ea1e30e4d48702883ba15db807a92dd43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Neurons - physiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hurwitz, Cole</creatorcontrib><creatorcontrib>Kudryashova, Nina</creatorcontrib><creatorcontrib>Onken, Arno</creatorcontrib><creatorcontrib>Hennig, Matthias H.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Current opinion in neurobiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hurwitz, Cole</au><au>Kudryashova, Nina</au><au>Onken, Arno</au><au>Hennig, Matthias H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Building population models for large-scale neural recordings: Opportunities and pitfalls</atitle><jtitle>Current opinion in neurobiology</jtitle><addtitle>Curr Opin Neurobiol</addtitle><date>2021-10</date><risdate>2021</risdate><volume>70</volume><spage>64</spage><epage>73</epage><pages>64-73</pages><issn>0959-4388</issn><eissn>1873-6882</eissn><abstract>Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>34411907</pmid><doi>10.1016/j.conb.2021.07.003</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-7387-5535</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0959-4388 |
| ispartof | Current opinion in neurobiology, 2021-10, Vol.70, p.64-73 |
| issn | 0959-4388 1873-6882 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2563427721 |
| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Neurons - physiology |
| title | Building population models for large-scale neural recordings: Opportunities and pitfalls |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T07%3A57%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Building%20population%20models%20for%20large-scale%20neural%20recordings:%20Opportunities%20and%20pitfalls&rft.jtitle=Current%20opinion%20in%20neurobiology&rft.au=Hurwitz,%20Cole&rft.date=2021-10&rft.volume=70&rft.spage=64&rft.epage=73&rft.pages=64-73&rft.issn=0959-4388&rft.eissn=1873-6882&rft_id=info:doi/10.1016/j.conb.2021.07.003&rft_dat=%3Cproquest_cross%3E2563427721%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2563427721&rft_id=info:pmid/34411907&rft_els_id=S0959438821000726&rfr_iscdi=true |