Battaglia, Demian

[["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","e1002176"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Hansel, David"],["dc.date.accessioned","2019-07-09T11:40:47Z"],["dc.date.available","2019-07-09T11:40:47Z"],["dc.date.issued","2011-10-01"],["dc.description.abstract","Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity. However, analysis of Local Field Potentials (LFPs) across different experiments reveals considerable diversity in the degree of oscillatory behavior of this induced activity. Contrast-dependent power enhancements can indeed occur over a broad band in the gamma frequency range and spectral peaks may not arise at all. Furthermore, even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. We show that the strength of the inter-layer coupling crucially affects this spatiotemporal structure. We predict that layer VI inactivation should induce global changes in the spectral properties of induced LFPs, reflecting their slower temporal decorrelation in the absence of inter-layer feedback. Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation."],["dc.format.extent","24"],["dc.identifier.doi","10.1371/journal.pcbi.1002176"],["dc.identifier.pmid","21998568"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11310"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58249"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1553-7358"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Computational Biology"],["dc.subject.mesh","Contrast Sensitivity"],["dc.subject.mesh","Electroencephalography"],["dc.subject.mesh","Electrophysiological Phenomena"],["dc.subject.mesh","Feedback, Physiological"],["dc.subject.mesh","Models, Neurological"],["dc.subject.mesh","Nerve Net"],["dc.subject.mesh","Nonlinear Dynamics"],["dc.subject.mesh","Photic Stimulation"],["dc.subject.mesh","Visual Cortex"],["dc.title","Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Bulletin of the American Physical Society"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Witt, Annette"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Gail, Alexander"],["dc.date.accessioned","2018-02-26T14:17:17Z"],["dc.date.available","2018-02-26T14:17:17Z"],["dc.date.issued","2013"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/12627"],["dc.language.iso","en"],["dc.notes","https://meetings.aps.org/Meeting/MAR13/Event/190986"],["dc.notes.status","fcwi"],["dc.relation.conference","American Physical Society March Meeting"],["dc.relation.eventend","2013-03-22"],["dc.relation.eventlocation","Baltimore, Md"],["dc.relation.eventstart","2013-03-18"],["dc.title","Paradoxical Behavior of Granger Causality"],["dc.type","conference_abstract"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1014"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Nature Neuroscience"],["dc.bibliographiccitation.lastpage","1022"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Palmigiano, Agostina"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Battaglia, Demian"],["dc.date.accessioned","2020-12-10T18:09:31Z"],["dc.date.available","2020-12-10T18:09:31Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1038/nn.4569"],["dc.identifier.eissn","1546-1726"],["dc.identifier.issn","1097-6256"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73680"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Flexible information routing by transient synchrony"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e98842"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Orlandi, Javier G."],["dc.contributor.author","Stetter, Olav"],["dc.contributor.author","Soriano, Jordi"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Battaglia, Demian"],["dc.date.accessioned","2018-11-07T09:38:58Z"],["dc.date.available","2018-11-07T09:38:58Z"],["dc.date.issued","2014"],["dc.description.abstract","Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron."],["dc.identifier.doi","10.1371/journal.pone.0098842"],["dc.identifier.isi","000341869000067"],["dc.identifier.pmid","24905689"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10181"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33177"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/330792/EU//DYNVIB"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Transfer Entropy Reconstruction and Labeling of Neuronal Connections from Simulated Calcium Imaging"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1002653"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Stetter, Olav"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Soriano, Jordi"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2018-11-07T09:07:33Z"],["dc.date.available","2018-11-07T09:07:33Z"],["dc.date.issued","2012"],["dc.description.abstract","A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local."],["dc.identifier.doi","10.1371/journal.pcbi.1002653"],["dc.identifier.isi","000308553500037"],["dc.identifier.pmid","22927808"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7870"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25820"],["dc.notes","This research was supported by the German Ministry for Education and Science (BMBF) via the Bernstein Center for Computational Neuroscience\r\n(BCCN) Go¨ ttingen (Grant No. 01GQ0430) and the Minerva Foundation, Mu¨nchen, Germany. JS acknowledges the financial support from the Ministerio de Ciencia\r\ne Innovacio´n (Spain) under projects FIS2009-07523 and FIS2010-21924- C02-02, and the Generalitat de Catalunya under project 2009-SGR-00014."],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.relation.issn","1553-734X"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 2.5"],["dc.rights.uri","http://creativecommons.org/licenses/by/2.5/"],["dc.title","Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","525"],["dc.bibliographiccitation.journal","NeuroImage"],["dc.bibliographiccitation.lastpage","535"],["dc.bibliographiccitation.volume","105"],["dc.contributor.author","Hansen, Enrique C. A."],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Spiegler, Andreas"],["dc.contributor.author","Deco, Gustavo"],["dc.contributor.author","Jirsa, Viktor K."],["dc.date.accessioned","2019-07-09T11:40:48Z"],["dc.date.available","2019-07-09T11:40:48Z"],["dc.date.issued","2015-01-15"],["dc.description.abstract","Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations."],["dc.identifier.doi","10.1016/j.neuroimage.2014.11.001"],["dc.identifier.pmid","25462790"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11344"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58254"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/60402"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/330792/EU//DYNVIB"],["dc.relation.euproject","BrainScales and Human Brain"],["dc.relation.euproject","DynViB"],["dc.relation.issn","1095-9572"],["dc.rights","CC BY-NC-SA 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-sa/3.0"],["dc.title","Functional connectivity dynamics: Modeling the switching behavior of the resting state."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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"]]

[["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"]]