Show simple item record

Dynamic Neural Fields with Intrinsic Plasticity

dc.contributor.authorStrub, Claudius
dc.contributor.authorSchöner, Gregor
dc.contributor.authorWörgötter, Florentin
dc.contributor.authorSandamirskaya, Yulia
dc.date.accessioned2019-12-17T12:37:34Z
dc.date.available2019-12-17T12:37:34Z
dc.date.issued2017de
dc.relation.ISSN1662-5188de
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/17010
dc.description.abstractDynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments.de
dc.language.isoengde
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/707373/EU//ECogNeTde
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectadaptation; dynamic neural fields; dynamics; intrinsic plasticityde
dc.subject.ddc530
dc.titleDynamic Neural Fields with Intrinsic Plasticityde
dc.typejournalArticlede
dc.identifier.doi10.3389/fncom.2017.00074
dc.type.versionpublishedVersionde
dc.relation.eISSN1662-5188
dc.bibliographicCitation.volume11de
dc.type.subtypejournalArticle
dc.identifier.pmid28912706
dc.bibliographicCitation.articlenumber74de
dc.description.statuspeerReviewedde
dc.bibliographicCitation.journalFrontiers in Computational Neurosciencede


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

These documents are avalilable under the license:
openAccess