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.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","3642"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","The Journal of Neuroscience"],["dc.bibliographiccitation.lastpage","3659"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Geisel, Theodor"],["dc.contributor.author","Karagiannis, Anastassios"],["dc.contributor.author","Gallopin, Thierry"],["dc.contributor.author","David, Csaba"],["dc.contributor.author","Battaglia, Demian"],["dc.contributor.author","Geoffroy, Helene"],["dc.contributor.author","Rossier, Jean"],["dc.contributor.author","Hillman, Elizabeth M. C."],["dc.contributor.author","Staiger, Jochen F."],["dc.contributor.author","Cauli, Bruno"],["dc.date.accessioned","2011-03-03T16:19:19Z"],["dc.date.accessioned","2021-10-11T11:34:43Z"],["dc.date.available","2011-03-03T16:19:19Z"],["dc.date.available","2021-10-11T11:34:43Z"],["dc.date.issued","2009"],["dc.description.abstract","Neuropeptide Y (NPY) is an abundant neuropeptide of the neocortex involved in numerous physiological and pathological processes. Because of the large electrophysiological, molecular, and morphological diversity of NPY-expressing neurons their precise identity remains unclear. To define distinct populations of NPY neurons we characterized, in acute slices of rat barrel cortex, 200 cortical neurons of layers I–IV by means of whole-cell patch-clamp recordings, biocytin labeling, and single-cell reverse transcriptase-PCR designed to probe for the expression of well established molecular markers for cortical neurons. To classify reliably cortical NPY neurons, we used and compared different unsupervised clustering algorithms based on laminar location and electrophysiological and molecular properties. These classification schemes confirmed that NPY neurons are nearly exclusively GABAergic and consistently disclosed three main types of NPY-expressing interneurons. (1) Neurogliaform-like neurons exhibiting a dense axonal arbor, were the most frequent and superficial, and substantially expressed the neuronal isoform of nitric oxide synthase. (2) Martinotti-like cells characterized by an ascending axon ramifying in layer I coexpressed somatostatin and were the most excitable type. (3) Among fast-spiking and parvalbumin-positive basket cells, NPY expression was correlated with pronounced spike latency. By clarifying the diversity of cortical NPY neurons, this study establishes a basis for future investigations aiming at elucidating their physiological roles."],["dc.identifier.doi","10.1523/JNEUROSCI.0058-09.2009"],["dc.identifier.fs","509361"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5924"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90691"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","0270-6474"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","http://goedoc.uni-goettingen.de/licenses"],["dc.subject","NPY-Expressing Neocortical Interneurons"],["dc.subject.ddc","610"],["dc.title","Classification of NPY-Expressing Neocortical Interneurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0146500"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Helmer, Markus"],["dc.contributor.author","Kozyrev, Vladislav"],["dc.contributor.author","Stephan, Valeska"],["dc.contributor.author","Treue, Stefan"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Battaglia, Demian"],["dc.date.accessioned","2017-09-07T11:43:32Z"],["dc.date.available","2017-09-07T11:43:32Z"],["dc.date.issued","2016"],["dc.description.abstract","Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches."],["dc.identifier.doi","10.1371/journal.pone.0146500"],["dc.identifier.gro","3151567"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12859"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8377"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]