Wolf, Fred

[["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","Gielen, S."],["dc.contributor.editor","Kappen, B."],["dc.date.accessioned","2017-09-07T11:46:18Z"],["dc.date.available","2017-09-07T11:46:18Z"],["dc.date.issued","2012"],["dc.description.abstract","We propose a mathematical description for the spatial organization of orientation preference in the visual cortex. In this approach the spatial pattern of orientation preference is predicted from position and chirality of its singularities (i.e. ”pinwheels”). The theory is derived from a few phenomenological principles characterizing the qualitative structure of orientation maps under the requirement of mathematical simplicity. A comparison with optically recorded images of cortical maps suggests that orientation preference can be predicted over a much larger spatial range than previously estimated on the basis of correlation measurements."],["dc.identifier.doi","10.1007/978-1-4471-2063-6_30"],["dc.identifier.gro","3151879"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8711"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer"],["dc.publisher.place","London"],["dc.relation.isbn","978-3-540-19839-0"],["dc.relation.ispartof","ICANN ’93"],["dc.title","Map Structure from Pinwheel Position"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","63"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.lastpage","75"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Öz, Pinar"],["dc.contributor.author","Huang, Min"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:14Z"],["dc.date.available","2017-09-07T11:46:14Z"],["dc.date.issued","2015"],["dc.description.abstract","Somatic action potentials (AP) of cortical pyramidal neurons have characteristically high onset-rapidness. The onset of the AP waveform is an indirect measure for the ability of a neuron to respond to temporally fast-changing stimuli. Theoretical studies on the pyramidal neuron response usually involves a canonical Hodgkin-Huxley (HH) type ion channel gating model, which assumes statistically independent gating of each individual channel. However, cooperative activity of ion channels are observed for various cell types, meaning that the activity (e.g. opening) of one channel triggers the activity (e.g. opening) of a certain fraction of its neighbors and hence, these groups of channels behave as a unit. In this study, we describe a multi-compartmental conductance-based model with cooperatively gating voltage-gated Na channels in the axon initial segment. Our model successfully reproduced the somatic sharp AP onsets of cortical pyramidal neurons. The onset latencies from the initiation site to the soma and the conduction velocities were also in agreement with the previous experimental studies."],["dc.identifier.doi","10.1007/s10827-015-0561-9"],["dc.identifier.gro","3151864"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8694"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","0929-5313"],["dc.title","Action potential initiation in a multi-compartmental model with cooperatively gating Na channels in the axon initial segment"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","194"],["dc.bibliographiccitation.journal","Journal of Neuroscience Methods"],["dc.bibliographiccitation.lastpage","203"],["dc.bibliographiccitation.volume","257"],["dc.contributor.author","Samhaber, Robert"],["dc.contributor.author","Schottdorf, Manuel"],["dc.contributor.author","El Hady, Ahmed"],["dc.contributor.author","Broeking, Kai"],["dc.contributor.author","Daus, Andreas"],["dc.contributor.author","Thielemann, Christiane"],["dc.contributor.author","Stühmer, Walter"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2018-11-07T10:19:24Z"],["dc.date.available","2018-11-07T10:19:24Z"],["dc.date.issued","2016"],["dc.description.abstract","Background: Multi-electrode arrays (MEAs) allow non-invasive multi-unit recording in-vitro from cultured neuronal networks. For sufficient neuronal growth and adhesion on such MEAs, substrate preparation is required. Plating of dissociated neurons on a uniformly prepared MEA's surface results in the formation of spatially extended random networks with substantial inter-sample variability. Such cultures are not optimally suited to study the relationship between defined structure and dynamics in neuronal networks. To overcome these shortcomings, neurons can be cultured with pre-defined topology by spatially structured surface modification. Spatially structuring a MEA surface accurately and reproducibly with the equipment of a typical cell-culture laboratory is challenging. New method: In this paper, we present a novel approach utilizing micro-contact printing (mu CP) combined with a custom-made device to accurately position patterns on MEAs with high precision. We call this technique AP-mu CP (accurate positioning micro-contact printing). Comparison with existing methods: Other approaches presented in the literature using mu CP for patterning either relied on facilities or techniques not readily available in a standard cell culture laboratory, or they did not specify means of precise pattern positioning. Conclusion: Here we present a relatively simple device for reproducible and precise patterning in a standard cell-culture laboratory setting. The patterned neuronal islands on MEAs provide a basis for high throughput electrophysiology to study the dynamics of single neurons and neuronal networks. (C) 2015 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.jneumeth.2015.09.022"],["dc.identifier.isi","000366224100020"],["dc.identifier.pmid","26432934"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41649"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1872-678X"],["dc.relation.issn","0165-0270"],["dc.title","Growing neuronal islands on multi-electrode arrays using an accurate positioning-mu CP device"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1335"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","1339"],["dc.bibliographiccitation.volume","38-40"],["dc.contributor.author","Kaschube, Matthias"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Löwel, Siegrid"],["dc.date.accessioned","2017-09-07T11:46:12Z"],["dc.date.available","2017-09-07T11:46:12Z"],["dc.date.issued","2001"],["dc.description.abstract","Our visual system preferentially groups contour segments that not only have the same orientation but are colinear as well. Long-range horizontal connections are thought to play an important role in context-dependent modifications of neuronal responses. Since the topology of these connections shows a close relation to the perceptual grouping criterion of colinearity, we tested whether the statistical properties of real world images are biased towards colinear contours. By wavelet analysis we detected contours in images of natural environments and calculated their spatial correlations. In urban as well as in natural environments, the correlations between colinear contour segments were larger than the correlations for parallel contour segments. These observations indicate that colinear contour segments dominate real world images and thus might bias the functional and structural development of our visual system."],["dc.identifier.doi","10.1016/s0925-2312(01)00493-3"],["dc.identifier.gro","3151855"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8684"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","0925-2312"],["dc.subject","Image analysis; Natural scenes; Wavelets; Visual cortex"],["dc.title","The prevalence of colinear contours in the real world"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","eabf2806"],["dc.bibliographiccitation.issue","24"],["dc.bibliographiccitation.journal","Science Advances"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Steffens, Heinz"],["dc.contributor.author","Mott, Alexander C."],["dc.contributor.author","Li, Siyuan"],["dc.contributor.author","Wegner, Waja"],["dc.contributor.author","Švehla, Pavel"],["dc.contributor.author","Kan, Vanessa W. Y."],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Liebscher, Sabine"],["dc.contributor.author","Willig, Katrin I."],["dc.date.accessioned","2021-07-05T14:57:45Z"],["dc.date.available","2021-07-05T14:57:45Z"],["dc.date.issued","2021"],["dc.description.abstract","Excitatory synapses on dendritic spines of pyramidal neurons are considered a central memory locus. To foster both continuous adaption and the storage of long-term information, spines need to be plastic and stable at the same time. Here, we advanced in vivo STED nanoscopy to superresolve distinct features of spines (head size and neck length/width) in mouse neocortex for up to 1 month. While LTP-dependent changes predict highly correlated modifications of spine geometry, we find both, uncorrelated and correlated dynamics, indicating multiple independent drivers of spine remodeling. The magnitude of this remodeling suggests substantial fluctuations in synaptic strength. Despite this high degree of volatility, all spine features exhibit persistent components that are maintained over long periods of time. Furthermore, chronic nanoscopy uncovers structural alterations in the cortex of a mouse model of neurodegeneration. Thus, at the nanoscale, stable dendritic spines exhibit a delicate balance of stability and volatility."],["dc.identifier.doi","10.1126/sciadv.abf2806"],["dc.identifier.pmid","34108204"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87727"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/265"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-441"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.eissn","2375-2548"],["dc.relation.workinggroup","RG Willig (Optical Nanoscopy in Neuroscience)"],["dc.relation.workinggroup","RG Wolf"],["dc.rights","CC BY-NC 4.0"],["dc.title","Stable but not rigid: Chronic in vivo STED nanoscopy reveals extensive remodeling of spines, indicating multiple drivers of plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","015108"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Chaos: An Interdisciplinary Journal of Nonlinear Science"],["dc.bibliographiccitation.lastpage","16"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:16Z"],["dc.date.available","2017-09-07T11:46:16Z"],["dc.date.issued","2006"],["dc.description.abstract","We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.The individual units of many physical systems, from the planets of our solar system to the atoms in a solid, typically interact continuously in time and without significant delay. Thus at every instant of time such a unit is influenced by the current state of its interaction partners. Moreover, particles of many-body systems are often considered to have very simple lattice topology (as in a crystal) or no prescribed topology at all (as in an ideal gas). Many important biological systems are drastically different: their units are interacting by sending and receiving pulses at discrete instances of time. Furthermore, biological systems often exhibit significant delays in the couplings and very complicated topologies of their interaction networks. Examples of such systems include neurons, which interact by stereotyped electrical pulses called action potentials or spikes; crickets, which chirp to communicate acoustically; populations of fireflies that interact by short light pulses. The combination of pulse-coupling, delays, and complicated network topology formally makes the dynamical system to be investigated a high dimensional, heterogeneous nonlinear hybrid system with delays. Here we present an exact analysis of aspects of the dynamics of such networks in the case of simple one-dimensional nonlinear interacting units. These systems are simple models for the collective dynamics of recurrent networks of spiking neurons. After briefly presenting stability results for the synchronous state, we show how to use the theory of random matrices to analytically predict the eigenvalue distribution of stability matrices and thus derive the speed of synchronization in terms of dynamical and network parameters. We find that networks of neural oscillators typically exhibit speed limits and cannot synchronize faster than a certain bound defined by the network topology."],["dc.identifier.doi","10.1063/1.2150775"],["dc.identifier.gro","3151890"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8722"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1054-1500"],["dc.title","Speed of synchronization in complex networks of neural oscillators: Analytic results based on Random Matrix Theory"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","821"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Wolf, F."],["dc.date.accessioned","2017-11-21T15:34:02Z"],["dc.date.available","2017-11-21T15:34:02Z"],["dc.date.issued","2001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/10167"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eventend","2001-11-15"],["dc.relation.eventlocation","San Diego"],["dc.relation.eventstart","2001-11-10"],["dc.relation.ispartof","Society for Neuroscience Abstracts"],["dc.title","Synchronization and Desynchronization in Neural Networks with General Connectivity"],["dc.type","conference_abstract"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","367"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","374"],["dc.bibliographiccitation.volume","26-27"],["dc.contributor.author","Ernst, Udo"],["dc.contributor.author","Pawelzik, Klaus"],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2017-09-07T11:46:13Z"],["dc.date.available","2017-09-07T11:46:13Z"],["dc.date.issued","1999"],["dc.description.abstract","In many experiments it has been found that stimuli outside the classical receptive-field of orientation-selective cells in the visual cortex can strongly modulate the response properties of these cells. Typically, stimuli with orientation contrasts lead to enhancement, whereas iso-orientation stimuli lead to suppression of the neuronal activity, but these phenomena in general depend in a complicate manner on various parameters like stimulus configuration, contrast, and geometry. In this contribution, we develop a simple theory for such non-classical receptive-field phenomena. We explain the basic mechanisms by a fixed-point analysis. Within this analysis, center-surround experiments can be described by trajectories in parameter space. This allows for a systematic variation of the coupling and stimulation constants. We show that the strength or sign of the enhancement or suppression should not only vary with the experimental paradigm but also with the position of the cell within the cortex. Our results suggest that non-classical receptive-field phenomena are mediated through orientation-specific lateral excitatory interactions."],["dc.identifier.doi","10.1016/s0925-2312(99)00026-0"],["dc.identifier.gro","3151865"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8695"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","0925-2312"],["dc.subject","Receptive fields; Visual cortex; Orientation contrast sensitivity; Fixed-point analysis; Center-surround phenomena"],["dc.title","Theory of non-classical receptive field phenomena in the visual cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1004602"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLOS Computational Biology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Schottdorf, Manuel"],["dc.contributor.author","Keil, Wolfgang"],["dc.contributor.author","Coppola, David"],["dc.contributor.author","White, Leonard E."],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:45:38Z"],["dc.date.available","2017-09-07T11:45:38Z"],["dc.date.issued","2015"],["dc.description.abstract","The architecture of iso-orientation domains in the primary visual cortex (V1) of placental carnivores and primates apparently follows species invariant quantitative laws. Dynamical optimization models assuming that neurons coordinate their stimulus preferences throughout cortical circuits linking millions of cells specifically predict these invariants. This might indicate that V1’s intrinsic connectome and its functional architecture adhere to a single optimization principle with high precision and robustness. To validate this hypothesis, it is critical to closely examine the quantitative predictions of alternative candidate theories. Random feedforward wiring within the retino-cortical pathway represents a conceptually appealing alternative to dynamical circuit optimization because random dimension-expanding projections are believed to generically exhibit computationally favorable properties for stimulus representations. Here, we ask whether the quantitative invariants of V1 architecture can be explained as a generic emergent property of random wiring. We generalize and examine the stochastic wiring model proposed by Ringach and coworkers, in which iso-orientation domains in the visual cortex arise through random feedforward connections between semi-regular mosaics of retinal ganglion cells (RGCs) and visual cortical neurons. We derive closed-form expressions for cortical receptive fields and domain layouts predicted by the model for perfectly hexagonal RGC mosaics. Including spatial disorder in the RGC positions considerably changes the domain layout properties as a function of disorder parameters such as position scatter and its correlations across the retina. However, independent of parameter choice, we find that the model predictions substantially deviate from the layout laws of iso-orientation domains observed experimentally. Considering random wiring with the currently most realistic model of RGC mosaic layouts, a pairwise interacting point process, the predicted layouts remain distinct from experimental observations and resemble Gaussian random fields. We conclude that V1 layout invariants are specific quantitative signatures of visual cortical optimization, which cannot be explained by generic random feedforward-wiring models."],["dc.identifier.doi","10.1371/journal.pcbi.1004602"],["dc.identifier.gro","3151833"],["dc.identifier.pmid","26575467"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12731"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8659"],["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 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Random Wiring, Ganglion Cell Mosaics, and the Functional Architecture of the Visual Cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e2114549118"],["dc.bibliographiccitation.issue","51"],["dc.bibliographiccitation.journal","Proceedings of the National Academy of Sciences"],["dc.bibliographiccitation.volume","118"],["dc.contributor.author","Merino, Ricardo Martins"],["dc.contributor.author","Leon-Pinzon, Carolina"],["dc.contributor.author","Stühmer, Walter"],["dc.contributor.author","Möck, Martin"],["dc.contributor.author","Staiger, Jochen F."],["dc.contributor.author","Wolf, Fred"],["dc.contributor.author","Neef, Andreas"],["dc.date.accessioned","2022-02-01T10:31:17Z"],["dc.date.available","2022-02-01T10:31:17Z"],["dc.date.issued","2021"],["dc.description.abstract","Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input\\’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons\\’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling."],["dc.description.abstract","Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling."],["dc.identifier.doi","10.1073/pnas.2114549118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98819"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.relation.eissn","1091-6490"],["dc.relation.issn","0027-8424"],["dc.rights.uri","https://www.pnas.org/site/aboutpnas/licenses.xhtml"],["dc.title","Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]