Wörgötter, Florentin Andreas

[["dc.bibliographiccitation.firstpage","153"],["dc.bibliographiccitation.journal","Neural Networks"],["dc.bibliographiccitation.lastpage","162"],["dc.bibliographiccitation.volume","123"],["dc.contributor.author","Herzog, Sebastian"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2020-12-10T15:20:27Z"],["dc.date.available","2020-12-10T15:20:27Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1016/j.neunet.2019.12.004"],["dc.identifier.issn","0893-6080"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72672"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Evolving artificial neural networks with feedback"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","134"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:38:27Z"],["dc.date.available","2018-11-07T08:38:27Z"],["dc.date.issued","2010"],["dc.description.abstract","Network activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structure of the network. To address this question a time-continuous treatment of plasticity is required. However, this is - even in simple recurrent network structures - currently not possible. Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. We use noisy periodic external input signals, which through the recurrent connections lead to complex actual ongoing inputs and observe that large stable ranges emerge in these networks without boundaries or weight-normalization. Somewhat counter-intuitively, we find that about 40% of these cases are obtained with a long-term potentiation-dominated STDP curve. Noise can reduce stability in some cases, but generally this does not occur. Instead stable domains are often enlarged. This study is a first step toward a better understanding of the ongoing interactions between activity and plasticity in recurrent networks using STDP. The results suggest that stability of (sub-)networks should generically be present also in larger structures."],["dc.identifier.doi","10.3389/fncom.2010.00134"],["dc.identifier.isi","000288499400005"],["dc.identifier.pmid","21152348"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18773"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Res Found"],["dc.relation.issn","1662-5188"],["dc.title","Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","UNSP 36"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:09:14Z"],["dc.date.available","2018-11-07T09:09:14Z"],["dc.date.issued","2012"],["dc.description.abstract","Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses toward stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties."],["dc.identifier.doi","10.3389/fncom.2012.00036"],["dc.identifier.fs","597278"],["dc.identifier.isi","000305330100001"],["dc.identifier.pmid","22719724"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7780"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26210"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Res Found"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience"],["dc.relation.issn","1662-5188"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Analysis of synaptic scaling in combination with Hebbian plasticity in several simple networks"],["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.artnumber","P287"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Auth, Johannes M."],["dc.contributor.author","Nachstedt, Timo"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2016-05-20T16:02:49Z"],["dc.date.accessioned","2021-10-11T11:34:21Z"],["dc.date.available","2016-05-20T16:02:49Z"],["dc.date.available","2021-10-11T11:34:21Z"],["dc.date.issued","2015"],["dc.date.updated","2016-05-20T16:02:49Z"],["dc.identifier.doi","10.1186/1471-2202-16-S1-P287"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13268"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90666"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.holder","Auth et al."],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Stimulus discrimination and association with Hebbian cell assemblies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","poster"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","P254"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Nachstedt, Timo"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2016-05-20T16:02:52Z"],["dc.date.accessioned","2021-10-11T11:34:33Z"],["dc.date.available","2016-05-20T16:02:52Z"],["dc.date.available","2021-10-11T11:34:33Z"],["dc.date.issued","2015"],["dc.date.updated","2016-05-20T16:02:52Z"],["dc.identifier.doi","10.1186/1471-2202-16-S1-P254"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13269"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90682"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.holder","Nachstedt et al."],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Towards a biological plausible model of the interaction of long-term memory and working memory"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","poster"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1003307"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Tsodyks, Misha"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:18:47Z"],["dc.date.available","2018-11-07T09:18:47Z"],["dc.date.issued","2013"],["dc.description.abstract","Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling - a slow process usually associated with the maintenance of activity homeostasis - combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short-from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes."],["dc.identifier.doi","10.1371/journal.pcbi.1003307"],["dc.identifier.isi","000330355300055"],["dc.identifier.pmid","24204240"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9440"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28483"],["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.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation"],["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.artnumber","P372"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2011-07-22T22:25:45Z"],["dc.date.accessioned","2011-07-23T15:34:56Z"],["dc.date.accessioned","2021-10-11T11:26:03Z"],["dc.date.available","2011-07-22T22:25:45Z"],["dc.date.available","2011-07-23T15:34:56Z"],["dc.date.available","2021-10-11T11:26:03Z"],["dc.date.issued","2011"],["dc.date.updated","2011-07-22T22:25:46Z"],["dc.identifier.doi","10.1186/1471-2202-12-S1-P372"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6832"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90535"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 2.0"],["dc.rights.access","openAccess"],["dc.rights.holder","et al.; licensee BioMed Central Ltd."],["dc.rights.uri","http://creativecommons.org/licenses/by/2.0/"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","Synaptic scaling generically stabilizes circuit connectivity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","11"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Grinke, Eduard"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:50:14Z"],["dc.date.available","2018-11-07T09:50:14Z"],["dc.date.issued","2015"],["dc.description.abstract","Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSI I with 19 DOFs to adaptively avoid obstacles and navigate in the real world."],["dc.identifier.doi","10.3389/fnbot.2015.00011"],["dc.identifier.isi","000370403900001"],["dc.identifier.pmid","26528176"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13197"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35672"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5218"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.title","Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot"],["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","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","O5"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2016-05-20T16:02:59Z"],["dc.date.accessioned","2021-10-11T11:34:33Z"],["dc.date.available","2016-05-20T16:02:59Z"],["dc.date.available","2021-10-11T11:34:33Z"],["dc.date.issued","2015"],["dc.date.updated","2016-05-20T16:02:59Z"],["dc.identifier.doi","10.1186/1471-2202-16-S1-O5"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13271"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90681"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.holder","Dasgupta et al."],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Self-organization of computation in neural systems by interaction between homeostatic and synaptic plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]