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List of all publications whose research was partially or wholly funded by the IM-CLeVeR project, ordered by year and then by author.

This folder holds the following references to publications, sorted by year and author.

There are 323 references in this bibliography folder.

Cartoni, E, Puglisi-Allegra, S, and Baldassarre, G (2013).
The three principles of action: a Pavlovian-instrumental transfer hypothesis
Frontiers in Behavioural Neuroscience, 7(153):e1-11.

Chandrashekhariah, P, Spina, G, and Triesch, J (2013).
Let it Learn: A curious vision system for autonomous object learning
In: Proceedings of the eighth International conference on computer vision theory and applications (VISAPP 2013).

Ciancio, AL, Zollo, L, Baldassarre, G, Caligiore, D, and Giglielmelli, E (2013).
The role of learning and kinematic features in dexterous manipulation: a comparative study with two robotic hands
International Journal of Advanced Robotic Systems.

Cipriani, D, Caligiore, D, Baldassarre, G, Triesch, J, and De Marsico, M (2013).
OGTA: Open gaze tracker and analyzer a remote low cost system based on off-the-shelf components and open source modular software
In: ECEM 2013 is the 17th European Conference on Eye Movements, ed. by Holmqvist Kenneth and Villanueva Arantxa.

Connolly, J, Condell, J, Curran, K, and Gardiner, P (2013).
Towards Joint Stiffness measurement of Rheumatoid Arthritis sufferers
In: Design4Health conference.

Connolly, J, Condell, J, Curran, K, Gardiner, P, O’Flynn, B, Sanchez, JT, and Angrove, P (2013).
Data glove design improvements for finger joint Range of Motion measurement
In: Design4Health conference.

Gandhi, V and McGinnity, M (2013).
Quantum neural network based surface EMG signal filtering for control of robotic hand
In: IJCNN.

Gurney, K, Lepora, N, Shah, A, Koene, A, and Redgrave, P (2013).
Action discovery and intrinsically motivation: a biologically constrained formalisation
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli, Marco, pp. 151-181, Springer-Verlag, Berlin.

He, H, McGinnity, M, Coleman, S, and Gardiner, B (2013).
Linguistic Decision Making For Robot Route Learning
In: IEEE Transactions on Neural Networks and Learning Systems.

Law, J, Shaw, P, and Lee, M (2013).
A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot
Autonomous Robots, 35(1):77-92.

Lee, M, Law, J, and Huelse, M (2013).
A developmental framework for cumulative learning robots
In: Computational and robotic model of the hierarchical organization of behavior, ed. by Baldassarre, G., Mirolli, M., pp. 177-212, Springer, Berlin.

Leitner, J, Harding, S, Chandrashekhariah, P, Frank, M, Forster, A, Triesch, J, and Schmidhuber, J (2013).
Learning Visual Object Detection and Localisation Using icVision
In: Biologically Inspired Cognitive Architectures - Extended versions of selected papers from the Third Annual Meeting of the BICA Society (BICA 2012), vol. 5, pp. 29-41, Elsevier.

Leitner, J, Harding, S, Forster, A, and Schmidhuber, J (2013).
Cartesian Genetic Programming for Image Processing and Robot Vision
In: IEEE Robotics & Automation Society Summer School on "Robot Vision and Applications".

Leitner, J, Harding, S, Frank, M, Forster, A, and Schmidhuber, J (2013).
An Integrated, Modular Framework for Computer Vision and Cognitive Robotics Research (icVision)
In: Biologically Inspired Cognitive Architectures 2012 - Proceedings of the Third Annual Meeting of the BICA Society , ed. by Antonio Chella, Roberto Pirrone, Rosario Sorbello, Kamilla Rún Jóhannsdóttir, vol. 196. Advances in Intelligent Systems and Computing.

Leitner, J, Harding, S, Frank, M, Forster, A, and Schmidhuber, J (2013).
Artificial Neural Networks For Spatial Perception: Towards Visual Object Localisation in Humanoid Robots
In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2013).

Leng, G, Ray, AK, McGinnity, M, Coleman, S, and Maguire, L (2013).
On line sliding window based self-organising fuzzy neural network for cognitive reasoning
In: Cognitive 2013..

Lonini, L, Dimitrakakis, C, Rothkopf, C, and Triesch, J (2013).
Generalization and Interference in Human Motor Control
In: Computational and Robotic Models of the Hierarchical Organization of Behavior, pp. 155-176, Springer-Verlag, Berlin.

Lonini, L, Forestier, S, Teulière, C, Zhao, Y, Shi, B, and Triesch, J (2013).
Robust active binocular vision through intrinsically motivated learning
Frontiers in Neurorobotics, 7(20).

Lonini, L, Zhao, Y, Chandrashekhariah, P, Shi, B, and Triesch, J (2013).
Autonomous learning of active multi-scale binocular vision
In: Proceedings of Third Joint IEEE International Conference on Development and learning and on Epigenetic Robotics (ICDL-EpiRob 2013)).

Luciw, M, Kompella, V, Kazerounian, S, and Schmidhuber, J (2013).
An intrinsic value system for developing multiple invariant representations with incremental slowness learning
Front. Neurorobot..

Mannella, F, Gurney, K, and Baldassarre, G (2013).
The nucleus accumbens as a nexus between values and goals in goal-directed behaviour: a review and a new hypothesis
Frontiers in Behavioural Neuroscience, 7:e1-29.

McBride, S, Hülse, M, and Lee, M (2013).
Identifying the computational requirements of an integrated top-down-bottom-up model for overt visual attention within an active vision system
PLoS ONE, 8(2).

Mirolli, M and Baldassarre, G (2013).
Functions and mechanisms of intrinsic motivations: the knowledge versus competence distinction
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 49-72, Springer Verlag, Berlin.

Mirolli, M, Baldassarre, G, and Santucci, V (2013).
Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcement driving both action acquisition and reward maximization: A simulated robotic study
Neural Networks, 39:40-51.

Nehmzow, U, Gatsoulis, Y, Kerr, E, Condell, J, Siddique, N, and McGinnity, M (2013).
Novelty Detection as an Intrinsic Motivation for Cumulative Learning Robots
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, Springer-Verlag, Berlin.