Witt, Annette

[["dc.bibliographiccitation.artnumber","e1002438"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Witt, Annette"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2017-09-07T11:46:13Z"],["dc.date.available","2017-09-07T11:46:13Z"],["dc.date.issued","2012"],["dc.description.abstract","Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities."],["dc.identifier.doi","10.1371/journal.pcbi.1002438"],["dc.identifier.gro","3151853"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7868"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8682"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Dynamic Effective Connectivity of Inter-Areal Brain Circuits"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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.artnumber","e0125785"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.lastpage","16"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Wadewitz, P."],["dc.contributor.author","Hammerschmidt, K."],["dc.contributor.author","Battaglia, D."],["dc.contributor.author","Witt, A."],["dc.contributor.author","Wolf, F."],["dc.contributor.author","Fischer, J."],["dc.date.accessioned","2017-09-07T11:45:38Z"],["dc.date.available","2017-09-07T11:45:38Z"],["dc.date.issued","2015"],["dc.description.abstract","To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individuals, populations and taxa. Progress in this field has been hampered by a lack of standard in methodology, however. One problem is that researchers may settle on different variables to characterize the calls, which may impact on the classification of calls. More important, there is no agreement how to best characterize the overall structure of the repertoire in terms of the amount of gradation within and between call types. Here, we address these challenges by examining 912 calls recorded from wild chacma baboons (Papio ursinus). We extracted 118 acoustic variables from spectrograms, from which we constructed different sets of acoustic features, containing 9, 38, and 118 variables; as well 19 factors derived from principal component analysis. We compared and validated the resulting classifications of k-means and hierarchical clustering. Datasets with a higher number of acoustic features lead to better clustering results than datasets with only a few features. The use of factors in the cluster analysis resulted in an extremely poor resolution of emerging call types. Another important finding is that none of the applied clustering methods gave strong support to a specific cluster solution. Instead, the cluster analysis revealed that within distinct call types, subtypes may exist. Because hard clustering methods are not well suited to capture such gradation within call types, we applied a fuzzy clustering algorithm. We found that this algorithm provides a detailed and quantitative description of the gradation within and between chacma baboon call types. In conclusion, we suggest that fuzzy clustering should be used in future studies to analyze the graded structure of vocal repertoires. Moreover, the use of factor analyses to reduce the number of acoustic variables should be discouraged."],["dc.identifier.doi","10.1371/journal.pone.0125785"],["dc.identifier.gro","3151834"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11824"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8660"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","final"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Characterizing Vocal Repertoires—Hard vs. Soft Classification Approaches"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]