Geisel, Theo

[["dc.bibliographiccitation.firstpage","1706"],["dc.bibliographiccitation.issue","10-12"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","1710"],["dc.bibliographiccitation.volume","70"],["dc.contributor.author","Schrobsdorff, Hecke"],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2018-11-07T11:02:05Z"],["dc.date.available","2018-11-07T11:02:05Z"],["dc.date.issued","2007"],["dc.description.abstract","We study a model of feature binding in prefrontal cortex which defers specific perceptual information to lower areas and merely maintains the identity of the combination. The model consists of three layers of pulse-coupled leaky integrate-and-fire neurons. Features are encoded by the location of sustained activity in the subordinate layers. The feature layers are excitatorily coupled to a superordinate layer that represents combinations of features by means of an oscillatory dynamics. The model accounts for effects such as the memorization of an object that was perceived only for a short period, illusory binding of simultaneous stimuli, and the limit of attentional capacity. The present paper discusses conditions for localized excitations in networks of integrate-and-fire neurons and considers the application to a dynamic link architecture. (c) 2006 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.neucom.2006.10.049"],["dc.identifier.isi","000247215300023"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51296"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.publisher.place","Amsterdam"],["dc.relation.conference","15th Annual Computational Neuroscience Meeting"],["dc.relation.eventlocation","Edinburgh, SCOTLAND"],["dc.relation.issn","0925-2312"],["dc.title","A feature-binding model with localized excitations"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","857"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Nature Physics"],["dc.bibliographiccitation.lastpage","860"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Levina, Anna"],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2018-11-07T10:49:51Z"],["dc.date.available","2018-11-07T10:49:51Z"],["dc.date.issued","2007"],["dc.description.abstract","Self-organized criticality(1) is one of the key concepts to describe the emergence of complexity in natural systems. The concept asserts that a system self-organizes into a critical state where system observables are distributed according to a power law. Prominent examples of self-organized critical dynamics include piling of granular media(2), plate tectonics(3) and stick-slip motion(4). Critical behaviour has been shown to bring about optimal computational capabilities(5), optimal transmission(6), storage of information(7) and sensitivity to sensory stimuli(8-10). In neuronal systems, the existence of critical avalanches was predicted(11) and later observed experimentally(6,12,13). However, whereas in the experiments generic critical avalanches were found, in the model of ref. 11 they only show up if the set of parameters is fine-tuned externally to a critical transition state. Here, we demonstrate analytically and numerically that by assuming (biologically more realistic) dynamical synapses(14) in a spiking neural network, the neuronal avalanches turn from an exceptional phenomenon into a typical and robust self-organized critical behaviour, if the total resources of neurotransmitter are sufficiently large."],["dc.identifier.doi","10.1038/nphys758"],["dc.identifier.isi","000251456900022"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/48524"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","1745-2473"],["dc.title","Dynamical synapses causing self-organized criticality in neural networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","150"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of the Korean Physical Society"],["dc.bibliographiccitation.lastpage","157"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Mayer, Norbert Michael"],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Asada, Minoru"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2018-11-07T11:06:59Z"],["dc.date.available","2018-11-07T11:06:59Z"],["dc.date.issued","2007"],["dc.description.abstract","The structure of neural maps in the primary visual cortex arises from the problem of representing a high-dimensional stimulus manifold on an essentially two-dimensional piece of cortical tissue. In order to treat the problem theoretically, stimuli are usually represented by a set of features, such as centroid position, orientation, spatial frequency, phase etc. Inputs to the cortex are, however, activity distributions over afferent nerve fibers; i.e., they require, in principle, a description as high-dimensional vectors. We study the relation between high-dimensional maps, which can be assumed to rely on a Euclidean geometry, and low-dimensional feature maps, which need to be formulated in Riemannian space in order to represent high-dimensional maps to a good accuracy. We show numerically that the Riemannian framework allows for a suggestive explanation of the abundance of typical structural units (\"pinwheels\") in feature maps emerging in the course of the adaptation process from an initially unstructured state."],["dc.identifier.isi","000243481700008"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52441"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Korean Physical Soc"],["dc.publisher.place","Seoul"],["dc.relation.conference","4th Dynamics Days Asia Pacific International Conference on Nonlinear Science (DDAP4)"],["dc.relation.eventlocation","Pohang, SOUTH KOREA"],["dc.relation.issn","0374-4884"],["dc.title","Pinwheel stability in a non-Euclidean model of pattern formation in the visual cortex"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]