Kulvicius, Tomas

[["dc.bibliographiccitation.firstpage","481"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.lastpage","500"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:08:36Z"],["dc.date.available","2018-11-07T11:08:36Z"],["dc.date.issued","2008"],["dc.description.abstract","Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Nevertheless, rats may also rely on auditory, olfactory and somatosensory stimuli for orientation. It is also known that rats can track odors or self-generated scent marks to find a food source. Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. We emphasize the importance of olfactory cues in place cell formation and show that the utility of environmental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation."],["dc.description.sponsorship","Biotechnology and Biological Sciences Research Council [BB/C516079/1]"],["dc.identifier.doi","10.1007/s10827-008-0090-x"],["dc.identifier.isi","000259438100005"],["dc.identifier.pmid","18431616"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/3067"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52823"],["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","Odor supported place cell model and goal navigation in rodents"],["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","145"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","IEEE Transactions on Robotics"],["dc.bibliographiccitation.lastpage","157"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Ning, KeJun"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:13:47Z"],["dc.date.available","2018-11-07T09:13:47Z"],["dc.date.issued","2012"],["dc.description.abstract","The generation of complex movement patterns, in particular, in cases where one needs to smoothly and accurately join trajectories in a dynamic way, is an important problem in robotics. This paper presents a novel joining method that is based on the modification of the original dynamic movement primitive formulation. The new method can reproduce the target trajectory with high accuracy regarding both the position and the velocity profile and produces smooth and natural transitions in position space, as well as in velocity space. The properties of the method are demonstrated by its application to simulated handwriting generation, which are also shown on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for joining of movement sequences, which has a high potential for all robotics applications where trajectory joining is required."],["dc.identifier.doi","10.1109/TRO.2011.2163863"],["dc.identifier.isi","000300188300012"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27247"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1552-3098"],["dc.title","Joining Movement Sequences: Modified Dynamic Movement Primitives for Robotics Applications Exemplified on Handwriting"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S1053811921008077"],["dc.bibliographiccitation.firstpage","118534"],["dc.bibliographiccitation.journal","NeuroImage"],["dc.bibliographiccitation.volume","243"],["dc.contributor.author","Pomp, Jennifer"],["dc.contributor.author","Heins, Nina"],["dc.contributor.author","Trempler, Ima"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Mecklenbrauck, Falko"],["dc.contributor.author","Wurm, Moritz F."],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Schubotz, Ricarda I."],["dc.date.accessioned","2021-10-01T09:57:38Z"],["dc.date.available","2021-10-01T09:57:38Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.neuroimage.2021.118534"],["dc.identifier.pii","S1053811921008077"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89881"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.issn","1053-8119"],["dc.title","Touching events predict human action segmentation in brain and behavior"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["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","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["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","617"],["dc.identifier.doi","10.1007/s10827-010-0216-9"],["dc.identifier.isi","000278406500018"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6799"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19787"],["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","Odour supported place cell model and goal navigation in rodents (vol 25, pg 481, 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.firstpage","92"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","107"],["dc.bibliographiccitation.volume","119"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Wörgötter, Florentin Andreas"],["dc.date.accessioned","2019-07-22T14:52:54Z"],["dc.date.available","2019-07-22T14:52:54Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.robot.2019.05.005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61837"],["dc.language.iso","en"],["dc.relation.issn","0921-8890"],["dc.title","Context-based affordance segmentation from 2D images for robot actions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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.firstpage","103854"],["dc.bibliographiccitation.journal","Research in Developmental Disabilities"],["dc.bibliographiccitation.volume","110"],["dc.contributor.author","Silva, Nelson"],["dc.contributor.author","Zhang, Dajie"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Gail, Alexander"],["dc.contributor.author","Barreiros, Carla"],["dc.contributor.author","Lindstaedt, Stefanie"],["dc.contributor.author","Kraft, Marc"],["dc.contributor.author","Bölte, Sven"],["dc.contributor.author","Poustka, Luise"],["dc.contributor.author","Nielsen-Saines, Karin"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Einspieler, Christa"],["dc.contributor.author","Marschik, Peter B."],["dc.date.accessioned","2021-04-14T08:28:50Z"],["dc.date.available","2021-04-14T08:28:50Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.ridd.2021.103854"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82720"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.issn","0891-4222"],["dc.title","The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Herzog, Sebastian"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2021-04-14T08:29:51Z"],["dc.date.available","2021-04-14T08:29:51Z"],["dc.date.issued","2021"],["dc.description.abstract","Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases \\u0026lt;10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (\\u0026lt;22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.3389/fnbot.2020.600984"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17791"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83006"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5218"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","9888"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Reich, Simon"],["dc.contributor.author","Zhang, Dajie"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Bölte, Sven"],["dc.contributor.author","Nielsen-Saines, Karin"],["dc.contributor.author","Pokorny, Florian B."],["dc.contributor.author","Peharz, Robert"],["dc.contributor.author","Poustka, Luise"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Marschik, Peter B."],["dc.date.accessioned","2021-06-01T10:50:43Z"],["dc.date.available","2021-06-01T10:50:43Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network’s architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1038/s41598-021-89347-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86760"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.title","Novel AI driven approach to classify infant motor functions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]