Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits
We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to...
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| creator | Mureşan, Raul C. Pipa, Gordon Wheeler, Diek W. |
| description | We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted. |
| doi_str_mv | 10.1007/11550822_25 |
| format | Conference Proceeding |
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The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540287523</identifier><identifier>ISBN: 9783540287520</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540287544</identifier><identifier>EISBN: 354028754X</identifier><identifier>DOI: 10.1007/11550822_25</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Classical Analysis Method ; Computer science; control theory; systems ; Connectionism. 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The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Classical Analysis Method</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Experimental Neuroscience</subject><subject>Firing Rate</subject><subject>Multiunit Recording</subject><subject>Subthreshold Oscillation</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540287523</isbn><isbn>9783540287520</isbn><isbn>9783540287544</isbn><isbn>354028754X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkEtLAzEUheMLrHVW_oHZuHAxevOaJO5KqQ-oCGrXIZNJSrTOlCQt9N87pQqezT2c83EXB6ErDLcYQNxhzDlIQjThR6hQQlLOgEjBGTtGI1xjXFHK1Am6-CsIPUUjoEAqJRg9R0VKnzCI8ZpROUKz99AtV65adCGXb872sR2CNNhtSCG79r6c2By2Ie_K0O2JTYyuy-VLsLG3IdpNyOkSnXmzSq74vWO0eJh9TJ-q-evj83Qyr9YEq1wZQY1XyjQSMHbgFaNN7duaAVjrXQuCANRgiXBCusYJ4bB3kuF2oIwkdIyuD3_XJlmz8tF0NiS9juHbxJ3GoqaUUzlwNwcuDVW3dFE3ff-VNAa931H_25H-ACHEX9s</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Mureşan, Raul C.</creator><creator>Pipa, Gordon</creator><creator>Wheeler, Diek W.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits</title><author>Mureşan, Raul C. ; Pipa, Gordon ; Wheeler, Diek W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-a73af99ab8011e0f943b6fd6400ccfed0720060c27e78ebe77e1fe841d6fda823</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Classical Analysis Method</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Experimental Neuroscience</topic><topic>Firing Rate</topic><topic>Multiunit Recording</topic><topic>Subthreshold Oscillation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mureşan, Raul C.</creatorcontrib><creatorcontrib>Pipa, Gordon</creatorcontrib><creatorcontrib>Wheeler, Diek W.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mureşan, Raul C.</au><au>Pipa, Gordon</au><au>Wheeler, Diek W.</au><au>Kacprzyk, Janusz</au><au>Oja, Erkki</au><au>Zadrożny, Sławomir</au><au>Duch, Włodzisław</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits</atitle><btitle>Artificial Neural Networks: Biological Inspirations – ICANN 2005</btitle><date>2005</date><risdate>2005</risdate><spage>153</spage><epage>159</epage><pages>153-159</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540287523</isbn><isbn>9783540287520</isbn><eisbn>9783540287544</eisbn><eisbn>354028754X</eisbn><abstract>We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11550822_25</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Artificial Neural Networks: Biological Inspirations – ICANN 2005, 2005, p.153-159 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17633538 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Classical Analysis Method Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Experimental Neuroscience Firing Rate Multiunit Recording Subthreshold Oscillation |
| title | Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits |
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