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dc.contributor.authorMartín Muñoz, Mario
dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorEspósito, Gennaro
dc.contributor.authorChávez, Diógenes
dc.contributor.authorContreras-Hernández, Enrique
dc.contributor.authorGlusman, Silvio
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.authorRudomín, Pablo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2017-05-17T09:21:54Z
dc.date.available2017-05-17T09:21:54Z
dc.date.issued2017-05-01
dc.identifier.citationMartín, M., Béjar, J., Espósito, G., Chávez , D., Contreras-Hernández, E., Glusman, S., Cortés, C., Rudomín, P. Markovian analysis of the sequential behavior of the spontaneous spinal cord dorsum potentials induced by acute nociceptive stimulation in the anesthetized cat. "Frontiers in computational neuroscience", 1 Maig 2017, vol. 11, article 32, p. 1-13.
dc.identifier.issn1662-5188
dc.identifier.urihttp://hdl.handle.net/2117/104539
dc.description.abstractIn a previous study we developed a Machine Learning procedure for the automatic identification and classification of spontaneous cord dorsum potentials (CDPs). This study further supported the proposal that in the anesthetized cat, the spontaneous CDPs recorded from different lumbar spinal segments are generated by a distributed network of dorsal horn neurons with structured (non-random) patterns of functional connectivity and that these configurations can be changed to other non-random and stable configurations after the noceptive stimulation produced by the intradermic injection of capsaicin in the anesthetized cat. Here we present a study showing that the sequence of identified forms of the spontaneous CDPs follows a Markov chain of at least order one. That is, the system has memory in the sense that the spontaneous activation of dorsal horn neuronal ensembles producing the CDPs is not independent of the most recent activity. We used this markovian property to build a procedure to identify portions of signals as belonging to a specific functional state of connectivity among the neuronal networks involved in the generation of the CDPs. We have tested this procedure during acute nociceptive stimulation produced by the intradermic injection of capsaicin in intact as well as spinalized preparations. Altogether, our results indicate that CDP sequences cannot be generated by a renewal stochastic process. Moreover, it is possible to describe some functional features of activity in the cord dorsum by modeling the CDP sequences as generated by a Markov order one stochastic process. Finally, these Markov models make possible to determine the functional state which produced a CDP sequence. The proposed identification procedures appear to be useful for the analysis of the sequential behavior of the ongoing CDPs recorded from different spinal segments in response to a variety of experimental procedures including the changes produced by acute nociceptive stimulation. They are envisaged as a useful tool to examine alterations of the patterns of functional connectivity between dorsal horn neurons under normal and different pathological conditions, an issue of potential clinical concern.
dc.format.extent13 p.
dc.language.isoeng
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshSpinal cord
dc.subject.lcshMachine learning
dc.subject.lcshMarkov processes
dc.subject.otherMarkovian analysis
dc.subject.otherNociceptive stimulation
dc.subject.otherCord dorsum potentials
dc.titleMarkovian analysis of the sequential behavior of the spontaneous spinal cord dorsum potentials induced by acute nociceptive stimulation in the anesthetized cat
dc.typeArticle
dc.subject.lemacMedul·la espinal
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMarkov, Processos de
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1109/PDP.2017.52
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://journal.frontiersin.org/article/10.3389/fncom.2017.00032/full
dc.rights.accessOpen Access
drac.iddocument20568188
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2014-56256-C2-2-P
upcommons.citation.authorMartin, M., Béjar, J., Espósito, G., Chávez , D., Contreras-Hernández, E., Glusman, S., Cortés, C., Rudomín, P.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameFrontiers in computational neuroscience
upcommons.citation.volume11
upcommons.citation.numberArticle 32
upcommons.citation.startingPage1
upcommons.citation.endingPage13
dc.identifier.pmid28507514


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution 3.0 Spain