Treue, Stefan

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Daliri, Mohammad Reza"],["dc.contributor.author","Kozyrev, Vladislav"],["dc.contributor.author","Treue, Stefan"],["dc.date.accessioned","2017-09-07T11:43:35Z"],["dc.date.available","2017-09-07T11:43:35Z"],["dc.date.issued","2016"],["dc.description.abstract","The magnitude of the attentional modulation of neuronal responses in visual cortex varies with stimulus contrast. Whether the strength of these attentional influences is similarly dependent on other stimulus properties is unknown. Here we report the effect of spatial attention on responses in the medial-temporal area (MT) of macaque visual cortex to moving random dots pattern of various motion coherences, i.e. signal-to-noise ratios. Our data show that allocating spatial attention causes a gain change in MT neurons. The magnitude of this attentional modulation is independent of the attended stimulus’ motion coherence, creating a multiplicative scaling of the neuron’s coherence-response function. This is consistent with the characteristics of gain models of attentional modulation and suggests that attention strengthens the neuronal representation of behaviorally relevant visual stimuli relative to unattended stimuli, but without affecting their signal-to-noise ratios."],["dc.identifier.doi","10.1038/srep27666"],["dc.identifier.gro","3151574"],["dc.identifier.pmid","27283275"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13397"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8384"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Attention enhances stimulus representations in macaque visual cortex without affecting their signal-to-noise level"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","141"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Li, Kang"],["dc.contributor.author","Kozyrev, Vladislav"],["dc.contributor.author","Kyllingsbæk, Søren"],["dc.contributor.author","Treue, Stefan"],["dc.contributor.author","Ditlevsen, Susanne"],["dc.contributor.author","Bundesen, Claus"],["dc.date.accessioned","2017-09-07T11:43:33Z"],["dc.date.available","2017-09-07T11:43:33Z"],["dc.date.issued","2016"],["dc.description.abstract","A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior."],["dc.identifier.doi","10.3389/fncom.2016.00141"],["dc.identifier.gro","3151576"],["dc.identifier.pmid","28082892"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14164"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8387"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1662-5188"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["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"]]

[["dc.bibliographiccitation.firstpage","12506"],["dc.bibliographiccitation.issue","25"],["dc.bibliographiccitation.journal","Proceedings of the National Academy of Sciences"],["dc.bibliographiccitation.lastpage","12515"],["dc.bibliographiccitation.volume","116"],["dc.contributor.author","Khamechian, Mohammad Bagher"],["dc.contributor.author","Kozyrev, Vladislav"],["dc.contributor.author","Treue, Stefan"],["dc.contributor.author","Esghaei, Moein"],["dc.contributor.author","Daliri, Mohammad Reza"],["dc.date.accessioned","2020-12-10T18:12:51Z"],["dc.date.available","2020-12-10T18:12:51Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1073/pnas.1819827116"],["dc.identifier.eissn","1091-6490"],["dc.identifier.issn","0027-8424"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16439"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74521"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e3000387"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","PLoS Biology"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Kozyrev, Vladislav"],["dc.contributor.author","Daliri, Mohammad Reza"],["dc.contributor.author","Schwedhelm, Philipp"],["dc.contributor.author","Treue, Stefan"],["dc.contributor.editor","Kohn, Adam"],["dc.date.accessioned","2020-12-10T18:42:03Z"],["dc.date.available","2020-12-10T18:42:03Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1371/journal.pbio.3000387"],["dc.identifier.eissn","1545-7885"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16462"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77790"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Strategic deployment of feature-based attentional gain in primate visual cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]