Geisel, Theo

[["dc.bibliographiccitation.firstpage","87"],["dc.bibliographiccitation.journal","Frontiers in computational neuroscience"],["dc.bibliographiccitation.lastpage","87"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Uhlig, Maximilian"],["dc.contributor.author","Levina, Anna"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Herrmann, J. Michael"],["dc.date.accessioned","2019-07-09T11:40:06Z"],["dc.date.available","2019-07-09T11:40:06Z"],["dc.date.issued","2013"],["dc.description.abstract","Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network only endowed with Hebbian learning does not allow for simultaneous information storage and criticality. However, the critical regime can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity."],["dc.identifier.doi","10.3389/fncom.2013.00087"],["dc.identifier.fs","597034"],["dc.identifier.pmid","23898261"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10673"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58094"],["dc.language.iso","en"],["dc.notes","This work is supported by the Federal Ministry of \r\nEducation and Research(BMBF)Germany under grant number \r\n01GQ1005B."],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1662-5188"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goedoc.uni-goettingen.de/licenses"],["dc.title","Critical dynamics in associative memory networks."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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","1087"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","The International Journal of Robotics Research"],["dc.bibliographiccitation.lastpage","1098"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Hass, J."],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2018-11-07T09:01:31Z"],["dc.date.available","2018-11-07T09:01:31Z"],["dc.date.issued","2006"],["dc.description.abstract","This paper reports how and to what extent the mass distribution of a passive dynamic walker can be tuned to maximize walking speed and stability. An exploration of the complete parameter space of a bipedal walker is performed by numerical optimization, and optimal manifolds are found in terms of speed, the form of which can be explained by a physical analysis of step periods. Stability, quantified by the minimal basin of attraction, is also shown to be high along these manifolds, but with a maximum at only moderate speeds. Furthermore, it is examined how speed and stability change on different ground slopes. The observed dependence of the stability measure oil the slope is consistent with the interpretation of the walking cycle as a feedback loop, which also provides an explanation for the destabilization of the gait at higher slopes. Regarding speed, an unexpected decrease at higher slopes is observed. This effect reveals another important feature of passive dynamic walking, a swing-back phase of the swing leg near the end of a step, which decreases walking speed on the one hand, but seems to be crucial for the stability of the gait on the other hand. In conclusion, maximal robustness and highest walking speed are shown to be partly conflicting objectives of optimization."],["dc.identifier.doi","10.1177/0278364906072449"],["dc.identifier.isi","000242442200003"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12974"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24446"],["dc.notes","This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively."],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Sage Publications Ltd"],["dc.relation.issn","1741-3176"],["dc.relation.issn","0278-3649"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Optimal mass distribution for passivity-based bipedal robots"],["dc.type","journal_article"],["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","P118"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Levina, Anna"],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2012-05-07T10:35:42Z"],["dc.date.accessioned","2021-10-27T13:21:15Z"],["dc.date.available","2012-05-07T10:35:42Z"],["dc.date.available","2021-10-27T13:21:15Z"],["dc.date.issued","2011"],["dc.identifier.doi","10.1186/1471-2202-12-S1-P118"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7581"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92005"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Neural dynamics and network topology interact to form critical avalanches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","2"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Levina, Anna"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Herrmann, J. Michael"],["dc.date.accessioned","2011-04-11T12:24:51Z"],["dc.date.accessioned","2021-10-11T11:34:49Z"],["dc.date.available","2011-04-11T12:24:51Z"],["dc.date.available","2021-10-11T11:34:49Z"],["dc.date.issued","2009"],["dc.identifier.citation","Levina, Anna; Geisel, Theo; Herrmann, J Michael (2009): Switching to criticality by synchronized input - BMC Neuroscience, Vol. 10, Nr. Suppl 1, p. P155-"],["dc.identifier.doi","10.1186/1471-2202-10-S1-P155"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6091"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90704"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","http://goedoc.uni-goettingen.de/licenses"],["dc.subject","synchronized input"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","Switching to criticality by synchronized input"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["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"]]

[["dc.bibliographiccitation.artnumber","P9"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Schrobsdorff, Hecke"],["dc.contributor.author","Ihrke, Matthias"],["dc.contributor.author","Behrendt, Jörg"],["dc.contributor.author","Herrmann, J. Michael"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Levina, Anna"],["dc.date.accessioned","2011-04-14T14:36:24Z"],["dc.date.accessioned","2021-10-11T11:34:45Z"],["dc.date.available","2011-04-14T14:36:24Z"],["dc.date.available","2021-10-11T11:34:45Z"],["dc.date.issued","2009"],["dc.identifier.citation","Schrobsdorff, Hecke; Ihrke, Matthias; Behrendt, Jörg; Herrmann, J Michael; Geisel, Theo; Levina, Anna (2009): Are age-related cognitive effects caused by optimization? - BMC Neuroscience, Vol. 10, Nr. Suppl 1, p. P9-"],["dc.identifier.doi","10.1186/1471-2202-10-S1-P9"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6135"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90695"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","http://goedoc.uni-goettingen.de/licenses"],["dc.subject","age-related cognitive effects"],["dc.subject.ddc","570"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","Are age-related cognitive effects caused by optimization?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]