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

This folder holds the following references to publications, filtered by year.

There are 72 references in this bibliography folder for 2013 year.

Bednark, JG, Reynolds, JN, Stafford, T, Redgrave, P, and Franz, EA (2013).
Creating a movement heuristic for voluntary action: Electrophysiological correlates of movement-outcome learning
Cortex, 49:771--780.

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).

Thirkettle, M, Walton, T, Shah, A, Gurney, K, Redgrave, P, and Stafford, T (2013).
The path to learning: Action acquisition is impaired when visual reinforcement signals must first access cortex
Behavioural Brain Research, 243:267--272.

Bolado-Gomez, R and Gurney, K (2013).
A biologically plausible embodied model of action discovery
Frontiers in Neurorobotics, 7:4.

Shaw, P, Law, J, and Lee, M (2013).
An evaluation of environmental constraints for biologically constrained development of gaze control on an iCub robot
PALADYN Journal of Behavioral Robotics, 3(3):147-155.

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.

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

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

Nichols, E, McDaid, L, and Siddique, N (2013).
Biologically Inspired SNN for Robot Control
In: IEEE Transactions on Cybernetics, vol. 43, pp. 115 - 128.

O’Flynn, B, Sanchez, JT, Angove, P, Connolly, J, Condell, J, and Curran, K (2013).
Wireless Smart Glove for arthritis rehabilitation
In: Smart Systems Integration conference.

Srivastava, R, Steunebrink, B, and Schmidhuber, J (2013).
First experiments with PowerPlay
In: Neural Networks.

Visalberghi, E and Fragaszy, D (2013).
The Etho-Cebus Project: Stone-tool use by wild capuchin monkeys
In: Tool Use in Animals: Cognition and Ecology , ed. by C. Sanz, J. Call, C. Boesch. Cambridge University Press, chap. 10, pp. 203-222.

Santucci, VG, Baldassarre, G, and Mirolli, M (2013).
Which is the best intrinsic motivation signal for learning multiple skills?
Frontiers in Neurorobotics, 7:e1-14.

Baldassarre, G (2013).
What are intrinsic motivations? A biological and computational perspective
In: Dagstuhl Report from Seminar 13072: Mechanisms of Ongoing Development in Cognitive Robotics, ed. by Fagard, J.; Grupen, R. A.; Guerin, F. & Krüger, N., vol. 3(2), pp. 60-61.

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).

Baldassarre, G, Mannella, F, Fiore, V, Redgrave, P, Gurney, K, and Mirolli, M (2013).
Intrinsically motivated action-outcome learning and goal-based action recall: A system-level bio-constrained computational model
Neural Networks, 41:168-187.

Baldassarre, G and Mirolli, M (2013).
Computational and robotic models of the hierarchical organization of behavior: an overview
In: Computational and Robotic Models of the Hierarchical Organisation of Behaviour, ed. by Baldassarre Gianluca and Mirolli Marco, Springer Verlag, Berlin.

Baldassarre, G and Mirolli, M (2013).
Decidiing which skill to learn when: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM)
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 257-278, Springer Verlag, Berlin.

Baldassarre, G and Mirolli, M (2013).
Intrinsically Motivated Learning Systems: An Overview
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 1-14, Springer Verlag, Berlin.

Barto, A, Mirolli, M, and Baldassarre, G (2013).
Novelty or suprise?
Frontiers in Psychology, 4(907):1-15.

Caligiore, D, Pezzulo G, Miall, C, and Baldassarre, G (2013).
The contribution of brain sub-cortical loops in the expression and acquisition of action understanding abilities
Neuroscience and Biobehavioral Reviews, 37(10):2504-2515.

Cartoni, E, Mannella, F, Puglisi-Allegra, S, and Baldassarre, G (2013).
A Bayesian model for a Pavlovian-instrumental transfer hypothesis
In: The First Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2013), ed. by Dayan Peter, Niv Yael, Phelps Liz, Roy Nick, Singh Satinder and Sutton Rich, pp. e1-5, Princeton, NJ, University of Princeton.

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.

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.

Ognibene, D, Catenacci Volpe, N, Pezzulo, G, and Baldassarre, G (2013).
Learning epistemic actions in model-free memory-free reinforcement learning: experiments with a neuro-robotic model
In: Second International Conference on Biomimetic and Biohybrid Systems. Proceedings , ed. by N. F. Lepora, A. Mura, H. G. Krapp, P. F. M. J. Verschure, T. J. Prescott, pp. 191-203, Berlin, Springer. Lecture Notes in Computer Science, vol. 8064.