Battaglia, Demian

[["dc.bibliographiccitation.journal","Frontiers in Neural Circuits"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Witt, Annette"],["dc.contributor.author","Palmigiano, Agostina"],["dc.contributor.author","Neef, Andreas"],["dc.contributor.author","El Hady, Ahmed"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Battaglia, Demian"],["dc.date.accessioned","2017-09-07T11:45:38Z"],["dc.date.available","2017-09-07T11:45:38Z"],["dc.date.issued","2013"],["dc.description.abstract","Dynamic oscillatory coherence is believed to play a central role in flexible communication between brain circuits. To test this communication-through-coherence hypothesis, experimental protocols that allow a reliable control of phase-relations between neuronal populations are needed. In this modeling study, we explore the potential of closed-loop optogenetic stimulation for the control of functional interactions mediated by oscillatory coherence. The theory of non-linear oscillators predicts that the efficacy of local stimulation will depend not only on the stimulation intensity but also on its timing relative to the ongoing oscillation in the target area. Induced phase-shifts are expected to be stronger when the stimulation is applied within specific narrow phase intervals. Conversely, stimulations with the same or even stronger intensity are less effective when timed randomly. Stimulation should thus be properly phased with respect to ongoing oscillations (in order to optimally perturb them) and the timing of the stimulation onset must be determined by a real-time phase analysis of simultaneously recorded local field potentials (LFPs). Here, we introduce an electrophysiologically calibrated model of Channelrhodopsin 2 (ChR2)-induced photocurrents, based on fits holding over two decades of light intensity. Through simulations of a neural population which undergoes coherent gamma oscillations—either spontaneously or as an effect of continuous optogenetic driving—we show that precisely-timed photostimulation pulses can be used to shift the phase of oscillation, even at transduction rates smaller than 25%. We consider then a canonic circuit with two inter-connected neural populations oscillating with gamma frequency in a phase-locked manner. We demonstrate that photostimulation pulses applied locally to a single population can induce, if precisely phased, a lasting reorganization of the phase-locking pattern and hence modify functional interactions between the two populations."],["dc.identifier.doi","10.3389/fncir.2013.00049"],["dc.identifier.fs","599401"],["dc.identifier.gro","3151827"],["dc.identifier.pmid","23616748"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10678"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8657"],["dc.language.iso","en"],["dc.notes","Financial support by the German Federal Ministry of Education and Research (BMBF) via the Bernstein Center for Computational Neuroscience—Göttingen (01GQ1005B, 01GQ0430, 01GQ07113), the Bernstein Focus Neurotechnology—Göttingen (01GQ0811) and the Bernstein Focus Visual Learning (01GQ0921, 01GQ0922), the German Israel Research Foundation and the VolkswagenStiftung (ZN2632) and the Deutsche Forschungsgemeinschaft through CRC-889 (906-17.1/2006)."],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1662-5110"],["dc.relation.orgunit","Fakultät für Physik"],["dc.subject.mesh","Action Potentials"],["dc.subject.mesh","Biological Clocks"],["dc.subject.mesh","Computational Biology"],["dc.subject.mesh","HEK293 Cells"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Neural Networks (Computer)"],["dc.subject.mesh","Optogenetics"],["dc.subject.mesh","Photic Stimulation"],["dc.subject.mesh","Random Allocation"],["dc.subject.mesh","Time Factors"],["dc.title","Controlling the oscillation phase through precisely timed closed-loop optogenetic stimulation: a computational study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","13"],["dc.bibliographiccitation.journal","Frontiers in neural circuits"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Karagiannis, Anastassios"],["dc.contributor.author","Gallopin, Thierry"],["dc.contributor.author","Gutch, Harold W."],["dc.contributor.author","Cauli, Bruno"],["dc.date.accessioned","2019-07-09T11:40:07Z"],["dc.date.available","2019-07-09T11:40:07Z"],["dc.date.issued","2013"],["dc.description.abstract","Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes."],["dc.identifier.doi","10.3389/fncir.2013.00013"],["dc.identifier.fs","599400"],["dc.identifier.pmid","23403725"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10677"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58096"],["dc.language.iso","en"],["dc.notes","Financial support by the German Federal Ministry of Education and Research(BMBF)via the Bernstein Center for Computational Neuroscience—Göttingen (01GQ1005B),by the Human Frontier Science Program (RGY0070/2007)and by the Agence Nationale pour la Recherche(ANR 2011MALZ00301)."],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1662-5110"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goedoc.uni-goettingen.de/licenses"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Databases, Factual"],["dc.subject.mesh","Fuzzy Logic"],["dc.subject.mesh","Male"],["dc.subject.mesh","Neurons"],["dc.subject.mesh","Random Allocation"],["dc.subject.mesh","Rats"],["dc.subject.mesh","Rats, Wistar"],["dc.subject.mesh","Somatosensory Cortex"],["dc.title","Beyond the frontiers of neuronal types."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]