Pawelzik, Klaus Richard

[["dc.bibliographiccitation.firstpage","97"],["dc.bibliographiccitation.lastpage","101"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Pawelzik, Klaus"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Kim, Douglas S."],["dc.contributor.author","Bonhoeffer, Tobias"],["dc.contributor.editor","Eeckman, Frank H."],["dc.date.accessioned","2017-09-07T11:45:41Z"],["dc.date.available","2017-09-07T11:45:41Z"],["dc.date.issued","2011"],["dc.description.abstract","We propose a mathematical description for the spatial organization of orientation preference in the visual cortex. The theory is derived from the principle of optimal smoothness and predicts the spatial pattern of orientation preference from position and chirality of its singularities (i.e. “pinwheels”). The model exhibits long range order in the sense that, given the configuration of singularities, the specification of orientation preference at a single location fixes the entire map. A comparison with optically recorded images of cortical maps suggests that orientation preference can indeed be predicted over a much larger spatial range than previously estimated on the basis of correlation measurements."],["dc.identifier.doi","10.1007/978-1-4615-2714-5_16"],["dc.identifier.gro","3151843"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8670"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","Springer"],["dc.publisher.place","Boston, MA"],["dc.relation.isbn","978-1-4613-6169-5"],["dc.relation.ispartof","Computation in Neurons and Neural Systems"],["dc.title","Optimal Smoothness of Orientation Preference Maps"],["dc.type","book_chapter"],["dc.type.internalPublication","no"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","103"],["dc.bibliographiccitation.lastpage","109"],["dc.contributor.author","Wolf, F."],["dc.contributor.author","Pawelzik, Klaus"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Deppisch, J."],["dc.contributor.editor","Eeckman, Frank H."],["dc.date.accessioned","2017-11-21T14:15:58Z"],["dc.date.available","2017-11-21T14:15:58Z"],["dc.date.issued","1994"],["dc.description.abstract","Oscillatory neuronal responses have been suspected to reveal assembly formation via synchronization. We analyse these data in terms of a hidden-state model, the parameters of which are estimated directly from experimental spike trains. The model characterizes the excitation dynamics of the network which is reflected by the stochastic activity of individual observed neurons. Our approach reproduces the spike dynamics observed in cat visual cortex and predicts the cross-correlations of units which belong to the same assembly."],["dc.identifier.doi","10.1007/978-1-4615-2714-5_17"],["dc.identifier.gro","3151869"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/10155"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","Springer"],["dc.publisher.place","Boston, MA"],["dc.relation.isbn","978-1-4613-6169-5"],["dc.relation.ispartof","Computation In Neurons And Neural System"],["dc.title","Network Dynamics and Correlated Spikes"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]