Publications by topic


Reviews of Dynamic Neural Fields:
Sandamirskaya, Y. Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures. Frontiers in Neuroscience, Vol. 7, pp. 276, 2013 (http://www.frontiersin.org/journal/10.3389/fnins.2013.00276/abstract) [pdf]

Sandamirskaya, Y.; Zibner, S.; Schneegans, S.; & Schöner, G.
Using Dynamic Field Theory to Extend the Embodiment Stance toward Higher Cognition. New Ideas in Psychology, 2013 (http://dx.doi.org/10.1016/j.newideapsych.2013.01.002) [pdf]

Neuromorphic cognitive architectures:

Kreiser, R.; Pienroj, P.; Renner, A. & Sandamirskay, Y. Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018 [accepted]

Martel, J. N.; Müller, L. K.; Carey, S. J.; Müller, J.; Sandamirskaya, Y. & Dudek, P. Real-Time Depth from Focus on a Programmable Focal Plane Processor. IEEE Transaction on Circuits and Systems--I, 2018, 65, 925-934 [pdf]


Martel, J. N.; Müller, J.; Conradt, J. & Sandamirskaya, Y.
An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors. EEE International Symposium on Circuits and Systems (ISCAS), 2018 [pdf]

Kreiser, R., Cartiglia, M., Martel, J. N. P., Conradt, J. & Sandamirskaya, Y. A Neuromorphic approach to path integration: a head direction spiking neural network with visually-driven reset. IEEE Symposium for Circuits and Systems, ISCAS, 2018 [pdf]

Kreiser, R.; Moraitis, T.; Sandamirskaya, Y. & Indiveri, G. On-chip unsupervised learning in Winner-Take-All networks of spiking neurons. Biological Circuits and Systems (BioCAS), 2017 [pdf]

Blum, H.; Dietmüller, A.; Milde, M.; Conradt, J.; Indiveri, G. & Sandamirskaya, Y. A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor. Robotics Science and Systems Conference, RSS, 2017 [pdf]


Milde, M. B.; Blum, H.; Dietmüller, A.; Sumislawska, D.; Conradt, J.; Indiveri, G. & Sandamirskaya, Y. Obstacle avoidance and target acquisition for robot navigation using a mixed signal analog/digital neuromorphic processing system. Frontiers in Neurorobotics, 2017. http://journal.frontiersin.org/article/10.3389/fnbot.2017.00028/full [pdf]

Milde, M.; Dietmüller, A.; Blum, H.; Indiveri, G.; & Sandamirskaya, Y. Obstacle avoidance and target acquisition in mobile robots equipped with neuromorphic sensory-processing systems. IEEE International Symposium on Circuits and Systems (ISCAS), 2017 [pdf]

Salt, L., Indiveri, G., & Sandamirskaya, Y. Obstacle avoidance with Locust Giant Looming Detector neuron: towards a neuromorphic UAV implementation. IEEE International Symposium on Circuits and Systems (ISCAS), 2017 [pdf]

Martel, J. & Sandamirskaya, Y. A Neuromorphic Approach for tracking using Dynamic Neural Fields on a Programmable Vision-chip. International Conference on Distributed and Smart Cameras (ICDSC), 2016 [pdf]

Learning:
Intrinsic plasticity:
Strub, C.; Schöner, G.; Wörgötter, F. & Sandamirskaya, Y. Dynamic Neural Fields with Intrinsic Plasticity. Frontiers in Computational Neuroscience, 11, 74, 2017 [pdf]

Timing:
Duran, B. & Sandamirskaya, Y. Learning Temporal Intervals with Neural Dynamics. IEEE Transactions on Cognitive and Developmental Systems. Issue 99, 2017. http://dx.doi.org/10.1109/TCDS.2017.2676839 [pdf]

Adaptation and Saccadic Eye Movements
Sandamirskaya, Y. & Storck, T. Neural-Dynamic Architecture for Looking: from Visual to Motor Target Representation for Memory Saccades. 12th IEEE International Conference on Development and Learning (ICDL), 2014 [pdf]

Bell, C.; Storck, T. & Sandamirskaya, Y.
Learning to Look: a Dynamic Neural Fields Architecture for Gaze Shift Generation. International Conference for Artificial Neural Networks (ICANN), 2014 [pdf]


Sandamirskaya, Y.; Conradt, J.
Learning Sensorimotor Transformations with Dynamic Neural Fields. International Conference on Artificial Neural Networks (ICANN), 2013 [pdf]

Sandamirskaya, Y.; Conradt, J.
Increasing Autonomy of Learning Sensorimotor Transformations with Dynamic Neural Fields. IEEE International Conference on Robotics and Automation (ICRA), Workshop on “Autonomous Learning -- from Machine Learning to Learning in Real-World Autonomous Systems”, 2013 [pdf]

Haptic Learning
Strub, C.; Wörgötter, F.; Ritter, H.; & Sandamirskaya, Y. Using Haptics to Extract Object Shape from Rotational Manipulations. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014 [pdf]

Strub, C.; Wörgötter, F.; Ritter, H.; & Sandamirskaya, Y.
Correcting Pose Estimates during Tactile Exploration of Object Shape: a Neuro-robotic Study. 12th IEEE International Conference on Development and Learning (ICDL), 2014 [pdf]

Neural-dynamic Reinforcement Learning
Lowe, R.; Almèr, A.; Billing, E.; Sandamirskaya, Y. & Balkenius, C. Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects. Biological Cybernetics, 2017, 11, 365-388 [pdf]

Luciw, M.; Sandamirskaya, Y.; Kazerounian, S.; Schmidhuber, J, & Schöner, G. Reinforcement and Shaping in Learning Action Sequences with Neural Dynamics. 12th IEEE International Conference on Development and Learning (ICDL), 2014 [pdf]

Lowe, R.; Sandamirskaya, Y.; & Billing, E.
A Neural Dynamic Model of Associative Two-Process Theory: The Differential Outcomes Effect and Infant Development. 12th IEEE International Conference on Development and Learning (ICDL), 2014, [pdf]

Kazerounian, S.; Luciw, M; Richter, M; & Sandamirskaya, Y. Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics. International Joint Conference on Neural Networks (IJCNN), 2013 [pdf]
Luciw, M.; Kazerounian, S.; Lakhmann, K.; Richter, M. & Sandamirskaya, Y.
Learning the Perceptual Conditions of Satisfaction of Elementary Behaviors. Robotics: Science and Systems (RSS), Workshop "Active Learning in Robotics: Exploration, Curiosity, and Interaction", 2013 [pdf]
Luciw, M.; Kazerounian, S.; Sandamirskaya, Y.; Schöner, G; & Schmidhuber, J.
Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics. Simulation of Adaptive Behavior (SAB) , 2014 [pdf]
Luciw, M.; Kazerounian, S.; Lakhmann, K.; Richter, M. & Sandamirskaya, Y.
Learning the Condition of Satisfaction of an Elementary Behavior in Dynamic Field Theory. Robotics Paladyn: Journal of Behavioral Robotics, Vol. 6, 2015 [pdf]
Sequence generation:
Billing, E.; Lowe, R. & Sandamirskaya, Y. Simultaneous Planning and Action: Neural-dynamic Sequencing of Elementary Behaviours in Robot Navigation. Adaptive Behavior, 9, 2015, 1-22 [pdf]

Lobato, D.; Sandamirskaya, Y.; Richter, M. & Schöner, G. Parsing of action sequences: A neural dynamics approach. Paladyn Journal of Behavioural Robotics, Vol. 6, 2015 [pdf]

Sandamirskaya, Y. & Burtsev, M. NARLE: Neurocognitive architecture for the autonomous task recognition, learning, and execution, BICA, 2015, 13 [pdf]

Duran, B.; Sandamirskaya, Y.; & Schöner, G.
A Dynamic Field architecture for generation of hierarchically organized sequences. International Conference on Artificial Neural Networks (ICANN), 2012 [pdf]

Duran, B & Sandamirskaya, Y.
Neural Dynamics of Hierarchically Organized Sequences: a Robotic Implementation. Proceedings of 2012 IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2012 [pdf]

Richter, M.; Sandamirskaya, Y.; & Schöner, G. A robotic architecture for action selection and behavioral organization inspired by human cognition. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012 [pdf]

Sandamirskaya, Y.; Richter, M. & Schöner, G.
A neural-dynamic architecture for behavioral organization of an embodied agent. IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011), 2011 [pdf]

Sandamirskaya, Y. & Schöner, G. An embodied account of serial order: How instabilities drive sequence generation. Neural Networks, Vol. 23, pp. 1164-1179 2010, (http://www.sciencedirect.com/science/article/pii/S0893608010001516) [pdf]

Sandamirskaya, Y. & Schöner, G. Serial order in an acting system: a multidimensional dynamic neural fields implementation. Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on, 2010 [pdf]

Sandamirskaya, Y. & Schöner, G. Dynamic Field Theory of Sequential Action: A Model and its Implementation on an Embodied Agent. Development and Learning, 2008. ICDL 2008. 7th IEEE International Conference on, 2008 [pdf]

Sandamirskaya, Y. & Schöner, G. Dynamic Field Theory and Embodied Communication. Modeling communication with robots and virtual humans, G. Wachsmuth, I. & Knoblich, G. (Eds.) Springer, 2006, 260-278 (http://link.springer.com/chapter/10.1007%2F978-3-540-79037-2_14?LI=true) [pdf]


Spatial language:
Richter, M.; Lins, J.; Schneegans, S.; Sandamirskaya, Y. & Schöner, G. Autonomous Neural Dynamics to Test Hypotheses in a Model of Spatial Language. The Annual Meeting of the Cognitive Science Society, CogSci, 2014 [pdf]

van Hengel, U.; Sandamirskaya, Y.; Schneegans, S. & Schöner, G.
A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy. 21st IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2012, 2012 [pdf]

Lipinski, J.; Schneegans, S.; Sandamirskaya, Y.; Spencer, J. & Schöner, G.
A Neurobehavioral Model of Flexible Spatial Language Behaviors. Journal of Experimental Psychology: Learning, Memory, and Cognition (JEP:LMC), 2011 (http://psycnet.apa.org/psycinfo/2011-08228-001/) [pdf]

Sandamirskaya, Y.; Lipinski, J.; Iossifidis, I. & Schöner, G.
Natural human-robot interaction through spatial language: a dynamic neural fields approach. 19th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, 2010, 600-607 [pdf]

Lipinski, J.; Sandamirskaya, Y. & Schöner, G.
Swing it to the Left, Swing it to the Right: Enacting Flexible Spatial Language Using a Neurodynamic Framework. Cognitive Neurodynamics, special issue on Language Dynamics, 2009, 3 (http://link.springer.com/article/10.1007%2Fs11571-009-9096-y?LI=true) [pdf]

Lipinski, J.; Sandamirskaya, Y. & Schöner, G.
Behaviorally Flexible Spatial Communication: Robotic Demonstrations of a Neurodynamic Framework. KI 2009, Lecture Notes in Artificial Intelligence, Mertsching, B.; Hund, M. & Z., A. (Eds.), Berlin: Springer-Verlag, 2009, 5803, 257-264 [pdf]

Lipinski, J.; Sandamirskaya, Y. & Schöner, G.
Flexible Spatial Language Behaviors: Developing a Neural Dynamic Theoretical Framework. 9th International Conference on Cognitive Modeling, ICCM 2009. Manchester, UK, 2009 [pdf]




Supervised MSc theses:

Müller, J.
Memory Stereo Depth Perception with Event Based Vision Sensors and Temporally Structured Light
D-ITET, ETH Zurich, May 2017

Renner, A.
Memory for serial order in spiking neural networks. MSc Thesis.
NSC Programm, INI, ETH Zurich and University of Zurich, Jan. 2017

English, G.
Spiking Neural Network Models for the Emergence of Patterned Activity in Grid Cell Populations. MSc Thesis.
NCS Programm, INI, ETH Zurich and University of Zurich, Oct. 2017

Parra Barrero, E.
Mismatch Detection Neural Circuit Applied to Navigation. MSc Thesis.
NSC Programm, INI, ETH Zurich and University of Zurich, Aug. 2017

Salt, L.
Optimising a Neuromorphic Locust Looming Detector for UAV Obstacle Avoidance. MSc Thesis.
School of Information Technology and Electrical Engineering, The University of Queensland, Nov. 2016