Tamosiunaite, Minija

[["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.artnumber","UNSP 1427"],["dc.bibliographiccitation.journal","Frontiers in Psychology"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Stein, Simon C."],["dc.contributor.author","Sutterlütti, Rahel M."],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2018-11-07T09:51:30Z"],["dc.date.available","2018-11-07T09:51:30Z"],["dc.date.issued","2015"],["dc.description.abstract","Objects usually consist of parts and the question arises whether there are perceptual features which allow breaking down an object into its fundamental parts without any additional (e.g., functional) information. As in the first paper of this sequence, we focus on the division of our world along convex to concave surface transitions. Here we are using machine vision to produce convex segments from 3D-scenes. We assume that a fundamental part is one, which we can easily name while at the same time there is no natural subdivision possible into smaller parts. Hence in this experiment we presented the computer vision generated segments to our participants and asked whether they can identify and name them. Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name ability. In addition, we observed that using other image-segmentation methods will not yield nameable entities. This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities. It appears that other or further subdivisions do not carry such a strong semantical link to our everyday language as there are no names for them."],["dc.description.sponsorship","European Community [270273]"],["dc.identifier.doi","10.3389/fpsyg.2015.01427"],["dc.identifier.isi","000362853400001"],["dc.identifier.pmid","26441797"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12372"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35929"],["dc.language.iso","en"],["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 S.A."],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience"],["dc.relation.eissn","1664-1078"],["dc.relation.issn","1664-1078"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Perceptual influence of elementary three-dimensional geometry: (2) fundamental object parts"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dc.type.version","published_version"],["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.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","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","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.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","176"],["dc.bibliographiccitation.journal","International Journal of Advanced Robotic Systems"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Chatterjee, Sromona"],["dc.contributor.author","Nachstedt, Timo"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Enomoto, Yoshihide"],["dc.contributor.author","Ariizumi, Ryo"],["dc.contributor.author","Matsuno, Fumitoshi"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:47:33Z"],["dc.date.available","2018-11-07T09:47:33Z"],["dc.date.issued","2015"],["dc.description.abstract","Motor primitives provide a modular organization to complex behaviours in both vertebrates and invertebrates. Inspired by this, here we generate motor primitives for a complex snake-like robot with screw-drive units, and thence chain and combine them, in order to provide a versatile, goal-directed locomotion for the robot. The behavioural primitives of the robot are generated using a reinforcement learning approach called \"Policy Improvement with Path Integrals\" (PI2). PI2 is numerically simple and has the ability to deal with high-dimensional systems. Here, PI2 is used to learn the robot's motor controls by finding proper locomotion control parameters, like joint angles and screw-drive unit velocities, in a coordinated manner for different goals. Thus, it is able to generate a large repertoire of motor primitives, which are selectively stored to form a primitive library. The learning process was performed using a simulated robot and the learned parameters were successfully transferred to the real robot. By selecting different primitives and properly chaining or combining them, along with parameter interpolation and sensory feedback techniques, the robot can handle tasks like achieving a single goal or multiple goals while avoiding obstacles, and compensating for a change to its body shape."],["dc.identifier.doi","10.5772/61621"],["dc.identifier.isi","000366622200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12801"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35136"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Intech Europe"],["dc.relation.issn","1729-8814"],["dc.relation.issn","1729-8806"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.title","Learning and Chaining of Motor Primitives for Goal-directed Locomotion of a Snake-like Robot with Screw-drive Units"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","249"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biological Cybernetics"],["dc.bibliographiccitation.lastpage","260"],["dc.bibliographiccitation.volume","100"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Asfour, Tamim"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:31:52Z"],["dc.date.available","2018-11-07T08:31:52Z"],["dc.date.issued","2009"],["dc.description.abstract","Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult."],["dc.description.sponsorship","European Commission [IST-FP6-IP-027657]"],["dc.identifier.doi","10.1007/s00422-009-0295-8"],["dc.identifier.isi","000264260600005"],["dc.identifier.pmid","19229556"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/3528"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17214"],["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","Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions"],["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"]]