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dc.contributor.authorHeiney, Kristine
dc.contributor.authorHuse Ramstad, Ola
dc.contributor.authorSandvig, Ioanna
dc.contributor.authorSandvig, Axel
dc.contributor.authorNichele, Stefano
dc.date.accessioned2020-02-26T09:32:31Z
dc.date.available2020-02-26T09:32:31Z
dc.date.created2020-02-21T15:42:35Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-2484-1
dc.identifier.urihttp://hdl.handle.net/11250/2643808
dc.description.abstractIn this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized—or supercritical—behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. In addition to enabling the identification of networks that are well-suited for computation, this analysis is expected to aid in the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofProceedings of IEEE Symposium Series on Computational Intelligence (SSCI2019)
dc.relation.urihttps://ieeexplore.ieee.org/document/9002693
dc.titleAssessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanchesnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber246-253nb_NO
dc.identifier.doihttps://doi.org/10.1109/SSCI44817.2019.9002693
dc.identifier.cristin1796627
dc.relation.projectNorges forskningsråd: 270961nb_NO
dc.description.localcode© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,65,30,0
cristin.unitnameInstitutt for nevromedisin og bevegelsesvitenskap
cristin.ispublishedfalse
cristin.fulltextoriginal
cristin.fulltextpostprint


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