Tamosiunaite, Minija

[["dc.bibliographiccitation.firstpage","255"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Biological Cybernetics"],["dc.bibliographiccitation.lastpage","271"],["dc.bibliographiccitation.volume","103"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:38:17Z"],["dc.date.available","2018-11-07T08:38:17Z"],["dc.date.issued","2010"],["dc.description.abstract","Understanding closed loop behavioral systems is a non-trivial problem, especially when they change during learning. Descriptions of closed loop systems in terms of information theory date back to the 1950s, however, there have been only a few attempts which take into account learning, mostly measuring information of inputs. In this study we analyze a specific type of closed loop system by looking at the input as well as the output space. For this, we investigate simulated agents that perform differential Hebbian learning (STDP). In the first part we show that analytical solutions can be found for the temporal development of such systems for relatively simple cases. In the second part of this study we try to answer the following question: How can we predict which system from a given class would be the best for a particular scenario? This question is addressed using energy, input/output ratio and entropy measures and investigating their development during learning. This way we can show that within well-specified scenarios there are indeed agents which are optimal with respect to their structure and adaptive properties."],["dc.identifier.doi","10.1007/s00422-010-0396-4"],["dc.identifier.isi","000281667700001"],["dc.identifier.pmid","20556620"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5158"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18732"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0340-1200"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Behavioral analysis of differential hebbian learning in closed-loop systems"],["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","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:42:48Z"],["dc.date.available","2018-11-07T08:42:48Z"],["dc.date.issued","2010"],["dc.format.extent","619"],["dc.identifier.doi","10.1007/s10827-010-0217-8"],["dc.identifier.isi","000278406500019"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6800"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19788"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning (vol 25, pg 562, 2008)"],["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","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:42:48Z"],["dc.date.available","2018-11-07T08:42:48Z"],["dc.date.issued","2010"],["dc.format.extent","621"],["dc.identifier.doi","10.1007/s10827-010-0218-7"],["dc.identifier.isi","000278406500020"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6801"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19789"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties (vol 23, pg 113, 2007)"],["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","e0266679"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Möller, Konstantin"],["dc.contributor.author","Kappel, David"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.editor","Albu, Felix"],["dc.date.accessioned","2022-06-01T09:39:44Z"],["dc.date.available","2022-06-01T09:39:44Z"],["dc.date.issued","2022"],["dc.description.abstract","Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.1371/journal.pone.0266679"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/108550"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-572"],["dc.relation.eissn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Differential Hebbian learning with time-continuous signals for active noise reduction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","unpublished"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","562"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.lastpage","582"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:08:37Z"],["dc.date.available","2018-11-07T11:08:37Z"],["dc.date.issued","2008"],["dc.description.abstract","A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat. The convergence properties of RL-algorithms are affected by the exploration patterns of the learner. Therefore, we first analyzed the path characteristics of freely exploring rats in a test arena. We found that straight path segments with mean length 23 cm up to a maximal length of 80 cm take up a significant proportion of the total paths. Thus, rat paths are biased as compared to random exploration. Next we designed a RL system that reproduces these specific path characteristics. Our model arena is covered by overlapping, probabilistically firing place fields (PF) of realistic size and coverage. Because convergence of RL-algorithms is also influenced by the state space characteristics, different PF-sizes and densities, leading to a different degree of overlap, were also investigated. The model rat learns finding a reward opposite to its starting point. We observed that the combination of biased straight exploration, overlapping coverage and probabilistic firing will strongly impair the convergence of learning. When the degree of randomness in the exploration is increased, convergence improves, but the distribution of straight path segments becomes unrealistic and paths become 'wiggly'. To mend this situation without affecting the path characteristic two additional mechanisms are implemented: A gradual drop of the learned weights (weight decay) and path length limitation, which prevents learning if the reward is not found after some expected time. Both mechanisms limit the memory of the system and thereby counteract effects of getting trapped on a wrong path. When using these strategies individually divergent cases get substantially reduced and for some parameter settings no divergence was found anymore at all. Using weight decay and path length limitation at the same time, convergence is not much improved but instead time to convergence increases as the memory limiting effect is getting too strong. The degree of improvement relies also on the size and degree of overlap (coverage density) in the place field system. The used combination of these two parameters leads to a trade-off between convergence and speed to convergence. Thus, this study suggests that the role of the PF-system in navigation learning cannot be considered independently from the animals' exploration pattern."],["dc.description.sponsorship","Biotechnology and Biological Sciences Research Council [BB/C516079/1]"],["dc.identifier.doi","10.1007/s10827-008-0094-6"],["dc.identifier.isi","000259438100009"],["dc.identifier.pmid","18446432"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/3066"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52824"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1573-6873"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning"],["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"]]