dc.contributor.author	Roy, Dipanjan
dc.contributor.author	Jirsa, Viktor
dc.date.accessioned	2019-10-11T11:42:23Z
dc.date.available	2019-10-11T11:42:23Z
dc.date.issued	2013-03-26
dc.date.updated	2019-09-27T06:13:37Z
dc.description.abstract	Computational models at different space-time scales allow us to understand the fundamental mechanisms that govern neural processes and relate uniquely these processes to neuroscience data. In this work, we propose a novel neurocomputational unit (a mesoscopic model which tell us about the interaction between local cortical nodes in a large scale neural mass model) of bursters that qualitatively captures the complex dynamics exhibited by a full network of parabolic bursting neurons. We observe that the temporal dynamics and fluctuation of mean synaptic action term exhibits a high degree of correlation with the spike/burst activity of our population. With heterogeneity in the applied drive and mean synaptic coupling derived from fast excitatory synapse approximations we observe long term behavior in our population dynamics such as partial oscillations, incoherence, and synchrony. In order to understand the origin of multistability at the population level as a function of mean synaptic coupling and heterogeneity in the firing rate threshold we employ a simple generative model for parabolic bursting recently proposed by Ghosh et al. (2009). Further, we use here a mean coupling formulated for fast spiking neurons for our analysis of generic model. Stability analysis of this mean field network allow us to identify all the relevant network states found in the detailed biophysical model. We derive here analytically several boundary solutions, a result which holds for any number of spikes per burst. These findings illustrate the role of oscillations occurring at slow time scales (bursts) on the global behavior of the network.	en
dc.description.sponsorship	EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleS	en
dc.identifier.eissn	1662-5188
dc.identifier.uri	https://depositonce.tu-berlin.de/handle/11303/10106
dc.identifier.uri	http://dx.doi.org/10.14279/depositonce-9094
dc.language.iso	en	en
dc.rights.uri	https://creativecommons.org/licenses/by/3.0/	en
dc.subject.ddc	600 Technik, Medizin, angewandte Wissenschaften	de
dc.subject.other	multispikes	en
dc.subject.other	self-organization	en
dc.subject.other	transients	en
dc.subject.other	firing rate	en
dc.subject.other	parabolic burst	en
dc.subject.other	network synchrony	en
dc.subject.other	generative model	en
dc.subject.other	oscillations	en
dc.title	Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion	en
dc.type	Article	en
dc.type.version	publishedVersion	en
dcterms.bibliographicCitation.articlenumber	20	en
dcterms.bibliographicCitation.doi	10.3389/fncom.2013.00020	en
dcterms.bibliographicCitation.journaltitle	Frontiers in Computational Neuroscience	en
dcterms.bibliographicCitation.originalpublishername	Frontiers Media S.A.	en
dcterms.bibliographicCitation.originalpublisherplace	Lausanne	en
dcterms.bibliographicCitation.volume	7	en
tub.accessrights.dnb	free	en
tub.affiliation	Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neuronale Informationsverarbeitung	de
tub.affiliation.faculty	Fak. 4 Elektrotechnik und Informatik	de
tub.affiliation.group	FG Neuronale Informationsverarbeitung	de
tub.affiliation.institute	Inst. Softwaretechnik und Theoretische Informatik	de
tub.publisher.universityorinstitution	Technische Universität Berlin	en

Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion

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