Fauth, Michael Jan

[["dc.bibliographiccitation.firstpage","44"],["dc.bibliographiccitation.journal","Artificial Intelligence"],["dc.bibliographiccitation.lastpage","65"],["dc.bibliographiccitation.volume","274"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Agostini, Alejandro"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2019-07-15T08:04:50Z"],["dc.date.available","2019-07-15T08:04:50Z"],["dc.date.issued","2019"],["dc.description.abstract","The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark."],["dc.identifier.doi","10.1016/j.artint.2018.12.009"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16274"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61490"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/731761/EU//IMAGINE"],["dc.relation.issn","0004-3702"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.title","Distributional semantics of objects in visual scenes in comparison to text"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Fauth, Michael Jan"],["dc.contributor.author","van Rossum, Mark CW"],["dc.date.accessioned","2020-12-10T18:48:07Z"],["dc.date.available","2020-12-10T18:48:07Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.7554/eLife.43717"],["dc.identifier.eissn","2050-084X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/79022"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Self-organized reactivation maintains and reinforces memories despite synaptic turnover"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","577"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Biology"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Miner, Daniel"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Fauth, Michael"],["dc.date.accessioned","2021-09-01T06:43:08Z"],["dc.date.available","2021-09-01T06:43:08Z"],["dc.date.issued","2021"],["dc.description.abstract","Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity."],["dc.description.abstract","Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity."],["dc.description.sponsorship","H2020 Future and Emerging Technologies"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/biology10070577"],["dc.identifier.pii","biology10070577"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89224"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.relation.eissn","2079-7737"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","CC BY 4.0"],["dc.title","Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission"],["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","e1004684"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2018-11-07T09:47:57Z"],["dc.date.available","2018-11-07T09:47:57Z"],["dc.date.issued","2015"],["dc.description.abstract","A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi-synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2016"],["dc.identifier.doi","10.1371/journal.pcbi.1004684"],["dc.identifier.isi","000368521900060"],["dc.identifier.pmid","26713858"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12700"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35209"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.relation.issn","1553-734X"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","1"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2014-07-02T10:46:45Z"],["dc.date.accessioned","2021-10-27T13:18:22Z"],["dc.date.available","2014-07-02T10:46:45Z"],["dc.date.available","2021-10-27T13:18:22Z"],["dc.date.issued","2013"],["dc.format.mimetype","application/pdf"],["dc.identifier.doi","10.1186/1471-2202-14-S1-P416"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10413"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91862"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.publisher","BioMed Central"],["dc.publisher.place","London"],["dc.relation.eissn","1471-2202"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Modelling the interaction of structural and synaptic plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1004031"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2018-11-07T10:03:47Z"],["dc.date.available","2018-11-07T10:03:47Z"],["dc.date.issued","2015"],["dc.description.abstract","Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3-8) of synapses. In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have. We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sublinearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems. Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities. Considering these activities as working point of the system and varying the pre-or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes. These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms."],["dc.identifier.doi","10.1371/journal.pcbi.1004031"],["dc.identifier.isi","000349309400020"],["dc.identifier.pmid","25590330"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11629"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38550"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.relation.issn","1553-734X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Bonilla-Quintana, Mayte"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","D’Este, Elisa"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Fauth, Michael"],["dc.date.accessioned","2021-04-14T08:28:35Z"],["dc.date.available","2021-04-14T08:28:35Z"],["dc.date.issued","2021"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1038/s41598-021-83331-9"],["dc.identifier.pmid","33597561"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82656"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/108"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C03: Modellierung der Fluktuation dendritischer Dornenfortsätze"],["dc.relation.eissn","2045-2322"],["dc.relation.orgunit","III. Physikalisches Institut - Biophysik"],["dc.relation.workinggroup","RG D’Este"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.relation.workinggroup","RG Wörgötter (Computational Neuroscience)"],["dc.rights","CC BY 4.0"],["dc.title","Reproducing asymmetrical spine shape fluctuations in a model of actin dynamics predicts self-organized criticality"],["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","75"],["dc.bibliographiccitation.journal","Frontiers in Neuroanatomy"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Fauth, Michael"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2018-11-07T10:12:36Z"],["dc.date.available","2018-11-07T10:12:36Z"],["dc.date.issued","2016"],["dc.description.abstract","The connectivity of the brain is continuously adjusted to new environmental influences by several activity-dependent adaptive processes. The most investigated adaptive mechanism is activity-dependent functional or synaptic plasticity regulating the transmission efficacy of existing synapses. Another important but less prominently discussed adaptive process is structural plasticity, which changes the connectivity by the formation and deletion of synapses. In this review, we show, based on experimental evidence, that structural plasticity can be classified similar to synaptic plasticity into two categories: (i) Hebbian structural plasticity, which leads to an increase (decrease) of the number of synapses during phases of high (low) neuronal activity and (ii) homeostatic structural plasticity, which balances these changes by removing and adding synapses. Furthermore, based on experimental and theoretical insights, we argue that each type of structural plasticity fulfills a different function. While Hebbian structural changes enhance memory lifetime, storage capacity, and memory robustness, homeostatic structural plasticity self-organizes the connectivity of the neural network to assure stability. However, the link between functional synaptic and structural plasticity as well as the detailed interactions between Hebbian and homeostatic structural plasticity are more complex. This implies even richer dynamics requiring further experimental and theoretical investigations."],["dc.identifier.doi","10.3389/fnana.2016.00075"],["dc.identifier.isi","000378615500001"],["dc.identifier.pmid","27445713"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13475"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/40271"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5129"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Opposing Effects of Neuronal Activity on Structural Plasticity"],["dc.type","review"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Synaptic Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Bonilla-Quintana, Mayte"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Fauth, Michael"],["dc.date.accessioned","2020-12-10T18:44:35Z"],["dc.date.available","2020-12-10T18:44:35Z"],["dc.date.issued","2020"],["dc.description.abstract","Dendritic spines are the morphological basis of excitatory synapses in the cortex and their size and shape correlates with functional synaptic properties. Recent experiments show that spines exhibit large shape fluctuations that are not related to activity-dependent plasticity but nonetheless might influence memory storage at their synapses. To investigate the determinants of such spontaneous fluctuations, we propose a mathematical model for the dynamics of the spine shape and analyze it in 2D—related to experimental microscopic imagery—and in 3D. We show that the spine shape is governed by a local imbalance between membrane tension and the expansive force from actin bundles that originates from discrete actin polymerization foci. Experiments have shown that only few such polymerization foci co-exist at any time in a spine, each having limited life time. The model shows that the momentarily existing set of such foci pushes the membrane along certain directions until foci are replaced and other directions may now be affected. We explore these relations in depth and use our model to predict shape and temporal characteristics of spines from the different biophysical parameters involved in actin polymerization. Approximating the model by a single recursive equation we finally demonstrate that the temporal evolution of the number of active foci is sufficient to predict the size of the model-spines. Thus, our model provides the first platform to study the relation between molecular and morphological properties of the spine with a high degree of biophysical detail."],["dc.identifier.doi","10.3389/fnsyn.2020.00009"],["dc.identifier.pmid","32218728"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17387"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78517"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/75"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C03: Modellierung der Fluktuation dendritischer Dornenfortsätze"],["dc.relation.eissn","1663-3563"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.relation.workinggroup","RG Wörgötter (Computational Neuroscience)"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Modeling the Shape of Synaptic Spines by Their Actin Dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]