Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments
Identifieur interne : 001B98 ( Istex/Corpus ); précédent : 001B97; suivant : 001B99Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments
Auteurs : M. Bove ; S. Martinoia ; G. Verreschi ; M. Giugliano ; M. GrattarolaSource :
- Biosensors and Bioelectronics [ 0956-5663 ] ; 1998.
Abstract
Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted in vitro. In this paper simulations related to experiments of the electrical activity recording by means of planar arrays of microtransducers coupled to networks of neurons are described. First a detailed model of single and synaptically connected neurons is given, appropriate to computer simulate the action potentials of neuronal populations. Then `realistic' signals are generated. These signals are intended to reproduce, both in shape and intensity, those recorded by a microelectrode array. Typical experimental conditions are considered, and a detailed analysis given, of the bioelectronic coupling and of its influence on the shape of the recorded signals. Finally, simulated experiments dealing with dorsal root ganglia neurons are described and analysed in comparison with experimental results reported in the literature and obtained in our own laboratory. The effectiveness of the planar microelectrode technique is briefly discussed.
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DOI: 10.1016/S0956-5663(98)00015-3
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Martinoia</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Verreschi, G" sort="Verreschi, G" uniqKey="Verreschi G" first="G." last="Verreschi">G. Verreschi</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Giugliano, M" sort="Giugliano, M" uniqKey="Giugliano M" first="M." last="Giugliano">M. 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Bove</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Martinoia, S" sort="Martinoia, S" uniqKey="Martinoia S" first="S." last="Martinoia">S. Martinoia</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Verreschi, G" sort="Verreschi, G" uniqKey="Verreschi G" first="G." last="Verreschi">G. Verreschi</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Giugliano, M" sort="Giugliano, M" uniqKey="Giugliano M" first="M." last="Giugliano">M. Giugliano</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author><author><name sortKey="Grattarola, M" sort="Grattarola, M" uniqKey="Grattarola M" first="M." last="Grattarola">M. Grattarola</name><affiliation><mods:affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Biosensors and Bioelectronics</title><title level="j" type="abbrev">BIOS</title><idno type="ISSN">0956-5663</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1998">1998</date><biblScope unit="volume">13</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="601">601</biblScope><biblScope unit="page" to="612">612</biblScope></imprint><idno type="ISSN">0956-5663</idno></series><idno type="istex">132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41</idno><idno type="DOI">10.1016/S0956-5663(98)00015-3</idno><idno type="PII">S0956-5663(98)00015-3</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0956-5663</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted in vitro. In this paper simulations related to experiments of the electrical activity recording by means of planar arrays of microtransducers coupled to networks of neurons are described. First a detailed model of single and synaptically connected neurons is given, appropriate to computer simulate the action potentials of neuronal populations. Then `realistic' signals are generated. These signals are intended to reproduce, both in shape and intensity, those recorded by a microelectrode array. Typical experimental conditions are considered, and a detailed analysis given, of the bioelectronic coupling and of its influence on the shape of the recorded signals. Finally, simulated experiments dealing with dorsal root ganglia neurons are described and analysed in comparison with experimental results reported in the literature and obtained in our own laboratory. The effectiveness of the planar microelectrode technique is briefly discussed.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>M. Bove</name><affiliations><json:string>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</json:string></affiliations></json:item><json:item><name>S. Martinoia</name><affiliations><json:string>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</json:string></affiliations></json:item><json:item><name>G. Verreschi</name><affiliations><json:string>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</json:string></affiliations></json:item><json:item><name>M. Giugliano</name><affiliations><json:string>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</json:string></affiliations></json:item><json:item><name>M. Grattarola</name><affiliations><json:string>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>In vitro networks of neurons</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>planar microelectrode arrays</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>neuron–electrode junction</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>synaptic connections</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>neuronal network dynamics</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>simulated neuronal networks</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>multi-signal processing tools</value></json:item></subject><language><json:string>eng</json:string></language><abstract>Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted in vitro. 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The effectiveness of the planar microelectrode technique is briefly discussed.</abstract><qualityIndicators><score>6.68</score><pdfVersion>1.2</pdfVersion><pdfPageSize>598 x 795 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>7</keywordCount><abstractCharCount>1053</abstractCharCount><pdfWordCount>4880</pdfWordCount><pdfCharCount>30413</pdfCharCount><pdfPageCount>12</pdfPageCount><abstractWordCount>150</abstractWordCount></qualityIndicators><title>Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</title><pii><json:string>S0956-5663(98)00015-3</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>13</volume><pii><json:string>S0956-5663(00)X0033-4</json:string></pii><pages><last>612</last><first>601</first></pages><issn><json:string>0956-5663</json:string></issn><issue>6</issue><genre><json:string>Journal</json:string></genre><language><json:string>unknown</json:string></language><title>Biosensors and Bioelectronics</title><publicationDate>1998</publicationDate></host><categories><wos><json:string>NANOSCIENCE & NANOTECHNOLOGY</json:string><json:string>CHEMISTRY, ANALYTICAL</json:string><json:string>ELECTROCHEMISTRY</json:string><json:string>BIOPHYSICS</json:string><json:string>BIOTECHNOLOGY & APPLIED MICROBIOLOGY</json:string></wos></categories><publicationDate>1998</publicationDate><copyrightDate>1998</copyrightDate><doi><json:string>10.1016/S0956-5663(98)00015-3</json:string></doi><id>132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41</id><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41/fulltext/pdf</uri></json:item><json:item><original>true</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41/fulltext/txt</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>ELSEVIER</p></availability><date>1998</date></publicationStmt><notesStmt><note type="content">Fig. 1: Sketch of the coupling between a compartimentalized microelectrode and neurons. Rseal is the sealing resistance referred to a single cell; Rspread is the spreading resistance referred to one of the four compartments into which the microelectrode is split. The four compartments are connected by metallic resistances (Rmt) and the output signal is connected to an ideal amplifier. Rel and Cel are the equivalent capacitance and resistance of the electrolyte–microelectrode compartment interface.</note><note type="content">Fig. 2: Sketch of the topography of the idealized network of neurons coupled to the microelectrode. All the neurons of the network were simulated by using a three-compartment model (see neuron 5 in detail). IHH represents the sum of the sodium and potassium currents related to Hodgkin and Huxley model equations; Rl and Vl are the leakage resistance and the leakage ion equilibrium potential, respectively; Cm is the cell membrane capacitance; Ri denotes the cytoplasmatic resistance connecting two adjacent compartments.</note><note type="content">Fig. 3: (a) Simulated signals of the electrically connected neurons of the idealized network (gap junction conductance Gj=10 000pS) coupled to the microelectrode with different coupling strenghts (Rseal2 =2MΩ; Rseal4 =10MΩ; Rseal6 =10MΩ; Rseal8=2MΩ). (b) Simulated signals of the electrically connected neurons of the idealized network in which neurons 5 and 9 were disconnected.</note><note type="content">Fig. 4: Simulation results of a recording from a local network characterized by (a) fast excitatory synapses (α=3ms−1mM−1; β=1ms−1; Esyn=30mV; gsyn=1nS); (b) slow excitatory synapses (α=1ms−1mM−1; β=0.1ms−1; Esyn=30mV; gsyn=2nS).</note><note type="content">Fig. 5: Sketch of the three electrodes coupled to a network of chemically connected neurons.</note><note type="content">Fig. 6: Simulation results of the membrane potential and of the signal transduction operated by the three microelectrodes in the isolated neurons condition. (a) neuron n1 and microelectrode El1; (b) neurons n2, n3 and n4 and microelectrode El2; (c) neurons n5 and n6 and microelectrode El3.</note><note type="content">Fig. 7: Simulation results of the membrane potentials and of the signal transduction operated by two microelectrodes in the locally connected neurons condition. (a) neurons n2, n3 and n4 and microelectrode El2; (b) neurons n5 and n6 and microelectrode El3.</note><note type="content">Fig. 8: Simulation results of the membrane potential and of the signal transduction operated by the three microelectrodes in the global connection condition.</note><note type="content">Fig. 9: Simulated responses of a neuron (arbitrarily chosen) of twenty neurons randomly connected in the five synaptic connection conditions.</note><note type="content">Fig. 10: (a) Bursting evaluation parameter and cross-correlation coefficient; (b) auto-correlation function evaluated for the different simulation phases (A corresponds to gsyn=0.05ns; B corresponds to gsyn=0.05nS; C corresponds to gsyn=0.1nS; D corresponds to gsyn=1nS; E corresponds to gsyn=3nS).</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</title><author><persName><forename type="first">M.</forename><surname>Bove</surname></persName><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation></author><author><persName><forename type="first">S.</forename><surname>Martinoia</surname></persName><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation></author><author><persName><forename type="first">G.</forename><surname>Verreschi</surname></persName><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation></author><author><persName><forename type="first">M.</forename><surname>Giugliano</surname></persName><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation></author><author><persName><forename type="first">M.</forename><surname>Grattarola</surname></persName><note type="correspondence"><p>Corresponding author. Tel.: +39-10-3532761; Fax: +39-10-3532133; E-mail:gratta@dibe.unige.it</p></note><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation></author></analytic><monogr><title level="j">Biosensors and Bioelectronics</title><title level="j" type="abbrev">BIOS</title><idno type="pISSN">0956-5663</idno><idno type="PII">S0956-5663(00)X0033-4</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1998"></date><biblScope unit="volume">13</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="601">601</biblScope><biblScope unit="page" to="612">612</biblScope></imprint></monogr><idno type="istex">132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41</idno><idno type="DOI">10.1016/S0956-5663(98)00015-3</idno><idno type="PII">S0956-5663(98)00015-3</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1998</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted in vitro. In this paper simulations related to experiments of the electrical activity recording by means of planar arrays of microtransducers coupled to networks of neurons are described. First a detailed model of single and synaptically connected neurons is given, appropriate to computer simulate the action potentials of neuronal populations. Then `realistic' signals are generated. These signals are intended to reproduce, both in shape and intensity, those recorded by a microelectrode array. Typical experimental conditions are considered, and a detailed analysis given, of the bioelectronic coupling and of its influence on the shape of the recorded signals. Finally, simulated experiments dealing with dorsal root ganglia neurons are described and analysed in comparison with experimental results reported in the literature and obtained in our own laboratory. The effectiveness of the planar microelectrode technique is briefly discussed.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>In vitro networks of neurons</term></item><item><term>planar microelectrode arrays</term></item><item><term>neuron–electrode junction</term></item><item><term>synaptic connections</term></item><item><term>neuronal network dynamics</term></item><item><term>simulated neuronal networks</term></item><item><term>multi-signal processing tools</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1998-03-05">Registration</change><change when="1998">Published</change></revisionDesc></teiHeader></istex:fulltextTEI></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3a" NDATA="IMAGE" name="gr3a"></istex:entity><istex:entity SYSTEM="gr3b" NDATA="IMAGE" name="gr3b"></istex:entity><istex:entity SYSTEM="gr4a" NDATA="IMAGE" name="gr4a"></istex:entity><istex:entity SYSTEM="gr4b" NDATA="IMAGE" name="gr4b"></istex:entity><istex:entity SYSTEM="gr5" NDATA="IMAGE" name="gr5"></istex:entity><istex:entity SYSTEM="gr6a" NDATA="IMAGE" name="gr6a"></istex:entity><istex:entity SYSTEM="gr6b" NDATA="IMAGE" name="gr6b"></istex:entity><istex:entity SYSTEM="gr6c" NDATA="IMAGE" name="gr6c"></istex:entity><istex:entity SYSTEM="gr7a" NDATA="IMAGE" name="gr7a"></istex:entity><istex:entity SYSTEM="gr7b" NDATA="IMAGE" name="gr7b"></istex:entity><istex:entity SYSTEM="gr8" NDATA="IMAGE" name="gr8"></istex:entity><istex:entity SYSTEM="gr9" NDATA="IMAGE" name="gr9"></istex:entity><istex:entity SYSTEM="gr10a" NDATA="IMAGE" name="gr10a"></istex:entity><istex:entity SYSTEM="gr10b" NDATA="IMAGE" name="gr10b"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>BIOS</jid><aid>306</aid><ce:pii>S0956-5663(98)00015-3</ce:pii><ce:doi>10.1016/S0956-5663(98)00015-3</ce:doi><ce:copyright year="1998" type="full-transfer">Elsevier Science Ltd</ce:copyright></item-info><head><ce:title>Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</ce:title><ce:author-group><ce:author><ce:given-name>M.</ce:given-name><ce:surname>Bove</ce:surname></ce:author><ce:author><ce:given-name>S.</ce:given-name><ce:surname>Martinoia</ce:surname></ce:author><ce:author><ce:given-name>G.</ce:given-name><ce:surname>Verreschi</ce:surname></ce:author><ce:author><ce:given-name>M.</ce:given-name><ce:surname>Giugliano</ce:surname></ce:author><ce:author><ce:given-name>M.</ce:given-name><ce:surname>Grattarola</ce:surname><ce:cross-ref refid="CORR1">*</ce:cross-ref></ce:author><ce:affiliation><ce:textfn>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author. Tel.: +39-10-3532761; Fax: +39-10-3532133; E-mail:gratta@dibe.unige.it</ce:text></ce:correspondence></ce:author-group><ce:date-received day="29" month="10" year="1997"></ce:date-received><ce:date-accepted day="5" month="3" year="1998"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted <ce:italic>in vitro</ce:italic>. In this paper simulations related to experiments of the electrical activity recording by means of planar arrays of microtransducers coupled to networks of neurons are described. First a detailed model of single and synaptically connected neurons is given, appropriate to computer simulate the action potentials of neuronal populations. Then `realistic' signals are generated. These signals are intended to reproduce, both in shape and intensity, those recorded by a microelectrode array. Typical experimental conditions are considered, and a detailed analysis given, of the bioelectronic coupling and of its influence on the shape of the recorded signals. Finally, simulated experiments dealing with dorsal root ganglia neurons are described and analysed in comparison with experimental results reported in the literature and obtained in our own laboratory. The effectiveness of the planar microelectrode technique is briefly discussed.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text><ce:italic>In vitro</ce:italic> networks of neurons</ce:text></ce:keyword><ce:keyword><ce:text>planar microelectrode arrays</ce:text></ce:keyword><ce:keyword><ce:text>neuron–electrode junction</ce:text></ce:keyword><ce:keyword><ce:text>synaptic connections</ce:text></ce:keyword><ce:keyword><ce:text>neuronal network dynamics</ce:text></ce:keyword><ce:keyword><ce:text>simulated neuronal networks</ce:text></ce:keyword><ce:keyword><ce:text>multi-signal processing tools</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Analysis of the signals generated by networks of neurons coupled to planar arrays of microtransducers in simulated experiments</title></titleInfo><name type="personal"><namePart type="given">M.</namePart><namePart type="family">Bove</namePart><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">S.</namePart><namePart type="family">Martinoia</namePart><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">G.</namePart><namePart type="family">Verreschi</namePart><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M.</namePart><namePart type="family">Giugliano</namePart><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M.</namePart><namePart type="family">Grattarola</namePart><affiliation>Bioelectronics and Neurobioengineering Group, Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, I-16145, Genoa, Italy</affiliation><description>Corresponding author. Tel.: +39-10-3532761; Fax: +39-10-3532133; E-mail:gratta@dibe.unige.it</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1998</dateIssued><dateValid encoding="w3cdtf">1998-03-05</dateValid><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Planar microelectrode arrays can be used to characterize the dynamics of networks of neurons reconstituted in vitro. In this paper simulations related to experiments of the electrical activity recording by means of planar arrays of microtransducers coupled to networks of neurons are described. First a detailed model of single and synaptically connected neurons is given, appropriate to computer simulate the action potentials of neuronal populations. Then `realistic' signals are generated. These signals are intended to reproduce, both in shape and intensity, those recorded by a microelectrode array. Typical experimental conditions are considered, and a detailed analysis given, of the bioelectronic coupling and of its influence on the shape of the recorded signals. Finally, simulated experiments dealing with dorsal root ganglia neurons are described and analysed in comparison with experimental results reported in the literature and obtained in our own laboratory. The effectiveness of the planar microelectrode technique is briefly discussed.</abstract><note type="content">Fig. 1: Sketch of the coupling between a compartimentalized microelectrode and neurons. Rseal is the sealing resistance referred to a single cell; Rspread is the spreading resistance referred to one of the four compartments into which the microelectrode is split. The four compartments are connected by metallic resistances (Rmt) and the output signal is connected to an ideal amplifier. Rel and Cel are the equivalent capacitance and resistance of the electrolyte–microelectrode compartment interface.</note><note type="content">Fig. 2: Sketch of the topography of the idealized network of neurons coupled to the microelectrode. All the neurons of the network were simulated by using a three-compartment model (see neuron 5 in detail). IHH represents the sum of the sodium and potassium currents related to Hodgkin and Huxley model equations; Rl and Vl are the leakage resistance and the leakage ion equilibrium potential, respectively; Cm is the cell membrane capacitance; Ri denotes the cytoplasmatic resistance connecting two adjacent compartments.</note><note type="content">Fig. 3: (a) Simulated signals of the electrically connected neurons of the idealized network (gap junction conductance Gj=10 000pS) coupled to the microelectrode with different coupling strenghts (Rseal2 =2MΩ; Rseal4 =10MΩ; Rseal6 =10MΩ; Rseal8=2MΩ). (b) Simulated signals of the electrically connected neurons of the idealized network in which neurons 5 and 9 were disconnected.</note><note type="content">Fig. 4: Simulation results of a recording from a local network characterized by (a) fast excitatory synapses (α=3ms−1mM−1; β=1ms−1; Esyn=30mV; gsyn=1nS); (b) slow excitatory synapses (α=1ms−1mM−1; β=0.1ms−1; Esyn=30mV; gsyn=2nS).</note><note type="content">Fig. 5: Sketch of the three electrodes coupled to a network of chemically connected neurons.</note><note type="content">Fig. 6: Simulation results of the membrane potential and of the signal transduction operated by the three microelectrodes in the isolated neurons condition. (a) neuron n1 and microelectrode El1; (b) neurons n2, n3 and n4 and microelectrode El2; (c) neurons n5 and n6 and microelectrode El3.</note><note type="content">Fig. 7: Simulation results of the membrane potentials and of the signal transduction operated by two microelectrodes in the locally connected neurons condition. (a) neurons n2, n3 and n4 and microelectrode El2; (b) neurons n5 and n6 and microelectrode El3.</note><note type="content">Fig. 8: Simulation results of the membrane potential and of the signal transduction operated by the three microelectrodes in the global connection condition.</note><note type="content">Fig. 9: Simulated responses of a neuron (arbitrarily chosen) of twenty neurons randomly connected in the five synaptic connection conditions.</note><note type="content">Fig. 10: (a) Bursting evaluation parameter and cross-correlation coefficient; (b) auto-correlation function evaluated for the different simulation phases (A corresponds to gsyn=0.05ns; B corresponds to gsyn=0.05nS; C corresponds to gsyn=0.1nS; D corresponds to gsyn=1nS; E corresponds to gsyn=3nS).</note><subject><genre>Keywords</genre><topic>In vitro networks of neurons</topic><topic>planar microelectrode arrays</topic><topic>neuron–electrode junction</topic><topic>synaptic connections</topic><topic>neuronal network dynamics</topic><topic>simulated neuronal networks</topic><topic>multi-signal processing tools</topic></subject><relatedItem type="host"><titleInfo><title>Biosensors and Bioelectronics</title></titleInfo><titleInfo type="abbreviated"><title>BIOS</title></titleInfo><genre type="Journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199809</dateIssued></originInfo><identifier type="ISSN">0956-5663</identifier><identifier type="PII">S0956-5663(00)X0033-4</identifier><part><date>199809</date><detail type="volume"><number>13</number><caption>vol.</caption></detail><detail type="issue"><number>6</number><caption>no.</caption></detail><extent unit="issue pages"><start>573</start><end>728</end></extent><extent unit="pages"><start>601</start><end>612</end></extent></part></relatedItem><identifier type="istex">132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41</identifier><identifier type="DOI">10.1016/S0956-5663(98)00015-3</identifier><identifier type="PII">S0956-5663(98)00015-3</identifier><accessCondition type="use and reproduction" contentType="">© 1998Elsevier Science Ltd</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Ltd, ©1998</recordOrigin></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/132B6C5605C8F6DCC23CB70ECC0C8D42AB011D41/enrichments/catWos"><teiHeader><profileDesc><textClass><classCode scheme="WOS">NANOSCIENCE & NANOTECHNOLOGY</classCode><classCode scheme="WOS">CHEMISTRY, ANALYTICAL</classCode><classCode scheme="WOS">ELECTROCHEMISTRY</classCode><classCode scheme="WOS">BIOPHYSICS</classCode><classCode scheme="WOS">BIOTECHNOLOGY & APPLIED MICROBIOLOGY</classCode></textClass></profileDesc></teiHeader></istex:catWosTEI></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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