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dc.contributor.authorDimitris Pinotsis*
dc.contributor.authorPeter Robinson*
dc.contributor.authorKarl Friston*
dc.contributor.authorPeter beim Graben*
dc.date.accessioned2021-02-11T20:48:05Z
dc.date.available2021-02-11T20:48:05Z
dc.date.issued2015*
dc.date.submitted2016-01-19 14:05:46*
dc.identifier18182*
dc.identifier.issn16648714*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/54476
dc.description.abstractBiophysical modelling of brain activity has a long and illustrious history and has recently profited from technological advances that furnish neuroimaging data at an unprecedented spatiotemporal resolution. Neuronal modelling is a very active area of research, with applications ranging from the characterization of neurobiological and cognitive processes, to constructing artificial brains in silico and building brain-machine interface and neuroprosthetic devices. Biophysical modelling has always benefited from interdisciplinary interactions between different and seemingly distant fields; ranging from mathematics and engineering to linguistics and psychology. This Research Topic aims to promote such interactions by promoting papers that contribute to a deeper understanding of neural activity as measured by fMRI or electrophysiology. In general, mean field models of neural activity can be divided into two classes: neural mass and neural field models. The main difference between these classes is that field models prescribe how a quantity characterizing neural activity (such as average depolarization of a neural population) evolves over both space and time as opposed to mass models, which characterize activity over time only; by assuming that all neurons in a population are located at (approximately) the same point. This Research Topic focuses on both classes of models and considers several aspects and their relative merits that: span from synapses to the whole brain; comparisons of their predictions with EEG and MEG spectra of spontaneous brain activity; evoked responses, seizures, and fitting data - to infer brain states and map physiological parameters.*
dc.languageEnglish*
dc.relation.ispartofseriesFrontiers Research Topics*
dc.subjectRC321-571*
dc.subjectQ1-390*
dc.subject.otherneural disorders*
dc.subject.otherself-organization*
dc.subject.otherElectroencephalogram*
dc.subject.otherneural networks*
dc.subject.otherElectrophysiology*
dc.subject.otherIntegro-differential equations*
dc.subject.otherneural field theory*
dc.subject.otherneural masses*
dc.subject.otheroscillations*
dc.subject.otheranaesthesia*
dc.titleNeural Masses and Fields: Modelling the Dynamics of Brain Activity*
dc.typebook
oapen.identifier.doi10.3389/978-2-88919-427-8*
oapen.relation.isPublishedBybf5ce210-e72e-4860-ba9b-c305640ff3ae*
virtual.oapen_relation_isPublishedBy.publisher_nameFrontiers Media SA
virtual.oapen_relation_isPublishedBy.publisher_websitewww.frontiersin.org
oapen.relation.isbn9782889194278*
oapen.pages237*


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