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Frequency variation analysis in neuronal cultures for stimulus response characterization

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dc.contributor.authorVal Calvo, Mikel
dc.contributor.authorAlegre Cortés, Javier
dc.contributor.authorFerrández Vicente, José Manuel
dc.contributor.authorFernández Jover, Eduardo
dc.contributor.authorVal Calvo, Inhar
dc.contributor.authorÁlvarez Sánchez, José Ramón
dc.contributor.authorPaz López, Félix de la
dc.date.accessioned2024-05-20T11:42:23Z
dc.date.available2024-05-20T11:42:23Z
dc.date.issued2019-01-03
dc.description.abstractIn vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence nonlinear and non-stationary. The usual approach for this characterization has been the use of the post-stimulus time histogram (PSTH). PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pairs produces a response in the other but not in the rest of the electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse inter-spike intervals, in each electrode. Machine learning methods can then be applied to distinguish the electrode being stimulated from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applicationsen
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1007/s00521-018-3942-y
dc.identifier.issn1433-3058
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12445
dc.journal.titleNeural Computing and Applications
dc.journal.volume32
dc.language.isoen
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsDissociated neurons
dc.subject.keywordsNeuronal stimulation
dc.subject.keywordsHybrots
dc.subject.keywordsMachine learning
dc.titleFrequency variation analysis in neuronal cultures for stimulus response characterizationes
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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