Manoonpong, Poramate

[["dc.bibliographiccitation.artnumber","39455"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Petersen, Dennis"],["dc.contributor.author","Kovalev, Alexander"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Gorb, Stanislav N."],["dc.contributor.author","Spinner, Marlene"],["dc.contributor.author","Heepe, Lars"],["dc.date.accessioned","2018-11-07T10:04:23Z"],["dc.date.available","2018-11-07T10:04:23Z"],["dc.date.issued","2016"],["dc.description.abstract","Based on the principles of morphological computation, we propose a novel approach that exploits the interaction between a passive anisotropic scale-like material (e.g., shark skin) and a non-smooth substrate to enhance locomotion efficiency of a robot walking on inclines. Real robot experiments show that passive tribologically-enhanced surfaces of the robot belly or foot allow the robot to grip on specific surfaces and move effectively with reduced energy consumption. Supplementing the robot experiments, we investigated tribological properties of the shark skin as well as its mechanical stability. It shows high frictional anisotropy due to an array of sloped denticles. The orientation of the denticles to the underlying collagenous material also strongly influences their mechanical interlocking with the substrate. This study not only opens up a new way of achieving energy-efficient legged robot locomotion but also provides a better understanding of the functionalities and mechanical properties of anisotropic surfaces. That understanding will assist developing new types of material for other real-world applications."],["dc.identifier.doi","10.1038/srep39455"],["dc.identifier.isi","000390523500001"],["dc.identifier.pmid","28008936"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14233"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38687"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2045-2322"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Enhanced Locomotion Efficiency of a Bio-inspired Walking Robot using Contact Surfaces with Frictional Anisotropy"],["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","10"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:51:28Z"],["dc.date.available","2018-11-07T09:51:28Z"],["dc.date.issued","2015"],["dc.description.abstract","Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots."],["dc.identifier.doi","10.3389/fnbot.2015.00010"],["dc.identifier.isi","000370403400001"],["dc.identifier.pmid","26441629"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13198"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35924"],["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.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots"],["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","301"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","The International Journal of Robotics Research"],["dc.bibliographiccitation.lastpage","331"],["dc.bibliographiccitation.volume","26"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Pasemann, Frank"],["dc.contributor.author","Roth, Hubert"],["dc.date.accessioned","2018-11-07T11:04:30Z"],["dc.date.available","2018-11-07T11:04:30Z"],["dc.date.issued","2007"],["dc.description.abstract","A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior of a four-legged and a six-legged walking machine. The controller utilizes discrete-time neuro-dynamics, and is of modular structure. One module is for processing sensor signals, one is a neural oscillator network serving as a central pattern generator and the third one is a so-called velocity regulating network. These modules are small and their structures and their functionalities are analyzable. In combination, they enable the machines to autonomously explore an unknown environment, to avoid obstacles, and to escape from corners or deadlock situations. The neurocontroller was developed and tested first using a physical simulation environment, and then it was successfully transferred to the physical walking machines. Locomotion is based on a gait where the diagonal legs are paired and move together e.g. trot gait for the four-legged walking machine and tripod gait for the six-legged walking machine. The controller developed is universal in the sense that it can easily be adapted to different types of even-legged walking machines without changing the internal structure and its parameters."],["dc.identifier.doi","10.1177/0278364906076263"],["dc.identifier.isi","000245177500005"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13000"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51858"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Sage Publications Ltd"],["dc.relation.issn","1741-3176"],["dc.relation.issn","0278-3649"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Modular reactive neurocontrol for biologically inspired walking machines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","20"],["dc.bibliographiccitation.journal","Frontiers in neurorobotics"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.date.accessioned","2019-07-09T11:44:54Z"],["dc.date.available","2019-07-09T11:44:54Z"],["dc.date.issued","2017"],["dc.description.abstract","Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates."],["dc.identifier.doi","10.3389/fnbot.2017.00020"],["dc.identifier.pmid","28446872"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14958"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59123"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/732266/EU//Plan4Act"],["dc.relation.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","006"],["dc.subject.ddc","573"],["dc.subject.ddc","612"],["dc.title","A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","12"],["dc.bibliographiccitation.journal","Frontiers in Neural Circuits"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:28:08Z"],["dc.date.available","2018-11-07T09:28:08Z"],["dc.date.issued","2013"],["dc.description.abstract","Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines."],["dc.identifier.doi","10.3389/fncir.2013.00012"],["dc.identifier.fs","604443"],["dc.identifier.isi","000314839800001"],["dc.identifier.pmid","23408775"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10221"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30705"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Research Foundation"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience"],["dc.relation.issn","1662-5110"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.title","Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines"],["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","3"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2018-11-07T09:44:55Z"],["dc.date.available","2018-11-07T09:44:55Z"],["dc.date.issued","2014"],["dc.description.abstract","Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment."],["dc.identifier.doi","10.3389/fnbot.2014.00003"],["dc.identifier.isi","000348807700001"],["dc.identifier.pmid","24523694"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11617"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34504"],["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.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots"],["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","e0192469"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Ambe, Yuichi"],["dc.contributor.author","Aoi, Shinya"],["dc.contributor.author","Nachstedt, Timo"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Matsuno, Fumitoshi"],["dc.contributor.editor","Cymbalyuk, Gennady"],["dc.date.accessioned","2020-12-10T18:42:05Z"],["dc.date.available","2020-12-10T18:42:05Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1371/journal.pone.0192469"],["dc.identifier.eissn","1932-6203"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15676"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77802"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits"],["dc.type","journal_article"],["dc.type.internalPublication","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.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.artnumber","UNSP 126"],["dc.bibliographiccitation.journal","Frontiers in Neural Circuits"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:33:23Z"],["dc.date.available","2018-11-07T09:33:23Z"],["dc.date.issued","2014"],["dc.description.abstract","Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.3389/fncir.2014.00126"],["dc.identifier.isi","000344065000001"],["dc.identifier.pmid","25389391"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11028"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31952"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Research Foundation"],["dc.relation.issn","1662-5110"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neurornodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control"],["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"]]