Kaschube, Matthias

[["dc.bibliographiccitation.artnumber","e1002466"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Reichl, Lars"],["dc.contributor.author","Heide, Dominik"],["dc.contributor.author","Löwel, Siegrid"],["dc.contributor.author","Crowley, Justin C."],["dc.contributor.author","Kaschube, Matthias"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:12Z"],["dc.date.available","2017-09-07T11:46:12Z"],["dc.date.issued","2012"],["dc.description.abstract","In the primary visual cortex of primates and carnivores, functional architecture can be characterized by maps of various stimulus features such as orientation preference (OP), ocular dominance (OD), and spatial frequency. It is a long-standing question in theoretical neuroscience whether the observed maps should be interpreted as optima of a specific energy functional that summarizes the design principles of cortical functional architecture. A rigorous evaluation of this optimization hypothesis is particularly demanded by recent evidence that the functional architecture of orientation columns precisely follows species invariant quantitative laws. Because it would be desirable to infer the form of such an optimization principle from the biological data, the optimization approach to explain cortical functional architecture raises the following questions: i) What are the genuine ground states of candidate energy functionals and how can they be calculated with precision and rigor? ii) How do differences in candidate optimization principles impact on the predicted map structure and conversely what can be learned about a hypothetical underlying optimization principle from observations on map structure? iii) Is there a way to analyze the coordinated organization of cortical maps predicted by optimization principles in general? To answer these questions we developed a general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference. From basic symmetry assumptions we obtain a comprehensive phenomenological classification of possible inter-map coupling energies and examine representative examples. We show that each individual coupling energy leads to a different class of OP solutions with different correlations among the maps such that inferences about the optimization principle from map layout appear viable. We systematically assess whether quantitative laws resembling experimental observations can result from the coordinated optimization of orientation columns with other feature maps."],["dc.identifier.doi","10.1371/journal.pcbi.1002466"],["dc.identifier.gro","3151847"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8433"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8676"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 2.5"],["dc.rights.uri","http://creativecommons.org/licenses/by/2.5/"],["dc.title","Coordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","P64"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Schnabel, Michael"],["dc.contributor.author","Kaschube, Matthias"],["dc.contributor.author","White, Leonard E."],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2011-04-15T06:26:42Z"],["dc.date.accessioned","2021-10-11T11:24:55Z"],["dc.date.available","2011-04-15T06:26:42Z"],["dc.date.available","2021-10-11T11:24:55Z"],["dc.date.issued","2009"],["dc.identifier.citation","Schnabel, Michael; Kaschube, Matthias; White, Leonard E; Wolf, Fred (2009): Pattern selection, pinwheel stability and the geometry of visual space - BMC Neuroscience, Vol. 10, Nr. Suppl 1, p. P64-"],["dc.identifier.doi","10.1186/1471-2202-10-S1-P64"],["dc.identifier.gro","3151838"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6137"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90532"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1471-2202"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","http://goedoc.uni-goettingen.de/licenses"],["dc.subject","Pattern selection; pinwheel"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","Pattern selection, pinwheel stability and the geometry of visual space"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","015009"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","New Journal of Physics"],["dc.bibliographiccitation.lastpage","20"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Kaschube, Matthias"],["dc.contributor.author","Schnabel, Michael"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:16Z"],["dc.date.available","2017-09-07T11:46:16Z"],["dc.date.issued","2008"],["dc.description.abstract","Self-organization of neural circuitry is an appealing framework for understanding cortical development, yet its applicability remains unconfirmed. Models for the self-organization of neural circuits have been proposed, but experimentally testable predictions of these models have been less clear. The visual cortex contains a large number of topological point defects, called pinwheels, which are detectable in experiments and therefore in principle well suited for testing predictions of self-organization empirically. Here, we analytically calculate the density of pinwheels predicted by a pattern formation model of visual cortical development. An important factor controlling the density of pinwheels in this model appears to be the presence of non-local long-range interactions, a property which distinguishes cortical circuits from many non-living systems in which self-organization has been studied. We show that in the limit where the range of these interactions is infinite, the average pinwheel density converges to π. Moreover, an average pinwheel density close to this value is robustly selected even for intermediate interaction ranges, a regime arguably covering interaction ranges in a wide range of different species. In conclusion, our paper provides the first direct theoretical demonstration and analysis of pinwheel density selection in models of cortical self-organization and suggests quantitatively probing this type of prediction in future high-precision experiments."],["dc.identifier.doi","10.1088/1367-2630/10/1/015009"],["dc.identifier.gro","3151875"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4346"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8706"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1367-2630"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Self-organization and the selection of pinwheel density in visual cortical development"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1002756"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Reichl, Lars"],["dc.contributor.author","Heide, Dominik"],["dc.contributor.author","Löwel, Siegrid"],["dc.contributor.author","Crowley, Justin C."],["dc.contributor.author","Kaschube, Matthias"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:12Z"],["dc.date.available","2017-09-07T11:46:12Z"],["dc.date.issued","2012"],["dc.description.abstract","In the juvenile brain, the synaptic architecture of the visual cortex remains in a state of flux for months after the natural onset of vision and the initial emergence of feature selectivity in visual cortical neurons. It is an attractive hypothesis that visual cortical architecture is shaped during this extended period of juvenile plasticity by the coordinated optimization of multiple visual cortical maps such as orientation preference (OP), ocular dominance (OD), spatial frequency, or direction preference. In part (I) of this study we introduced a class of analytically tractable coordinated optimization models and solved representative examples, in which a spatially complex organization of the OP map is induced by interactions between the maps. We found that these solutions near symmetry breaking threshold predict a highly ordered map layout. Here we examine the time course of the convergence towards attractor states and optima of these models. In particular, we determine the timescales on which map optimization takes place and how these timescales can be compared to those of visual cortical development and plasticity. We also assess whether our models exhibit biologically more realistic, spatially irregular solutions at a finite distance from threshold, when the spatial periodicities of the two maps are detuned and when considering more than 2 feature dimensions. We show that, although maps typically undergo substantial rearrangement, no other solutions than pinwheel crystals and stripes dominate in the emerging layouts. Pinwheel crystallization takes place on a rather short timescale and can also occur for detuned wavelengths of different maps. Our numerical results thus support the view that neither minimal energy states nor intermediate transient states of our coordinated optimization models successfully explain the architecture of the visual cortex. We discuss several alternative scenarios that may improve the agreement between model solutions and biological observations."],["dc.identifier.doi","10.1371/journal.pcbi.1002756"],["dc.identifier.gro","3151848"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8432"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8677"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 2.5"],["dc.rights.uri","http://creativecommons.org/licenses/by/2.5/"],["dc.title","Coordinated Optimization of Visual Cortical Maps (II) Numerical Studies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]