A. Gepperth, Efficient online bootstrapping of sensory representations, Neural Networks, vol.41, 2012.
DOI : 10.1016/j.neunet.2012.11.002

URL : https://hal.archives-ouvertes.fr/hal-00763660

M. Lefort and A. Gepperth, PROPRE: PROjection and PREdiction for multimodal correlations learning. An application to pedestrians visual data discrimination, 2014 International Joint Conference on Neural Networks (IJCNN), 2014.
DOI : 10.1109/IJCNN.2014.6889904

URL : https://hal.archives-ouvertes.fr/hal-01061662

S. Kirstein, H. Wersing, and E. Körner, Towards autonomous bootstrapping for life-long learning categorization tasks, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2010.
DOI : 10.1109/IJCNN.2010.5596344

P. Oudeyer, Developmental robotics, Encyclopedia of the Sciences of Learning, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00652123

B. Ridge, D. Skocaj, and A. Leonardis, Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems, 2010 IEEE International Conference on Robotics and Automation, pp.5047-5054, 2010.
DOI : 10.1109/ROBOT.2010.5509544

I. Bernstein, M. Clark, and B. Edelstein, Effects of an auditory signal on visual reaction time., Journal of Experimental Psychology, vol.80, issue.3, Pt.1, p.567, 1969.
DOI : 10.1037/h0027444

M. Doyle and R. Snowden, Identification of visual stimuli is improved by accompanying auditory stimuli: The role of eye movements and sound location, Perception, vol.30, issue.7, pp.795-810, 2001.
DOI : 10.1068/p3126

L. Shams and A. Seitz, Benefits of multisensory learning, Trends in Cognitive Sciences, vol.12, issue.11, pp.411-417, 2008.
DOI : 10.1016/j.tics.2008.07.006

M. Mossio and D. Taraborelli, Action-dependent perceptual invariants: From ecological to sensorimotor approaches, Consciousness and Cognition, vol.17, issue.4, pp.1324-1340, 2008.
DOI : 10.1016/j.concog.2007.12.003

URL : https://hal.archives-ouvertes.fr/halshs-00791131

E. Kandel, J. Schwartz, T. Jessell, S. Siegelbaum, and A. Hudspeth, Principles of neural science, 1991.

K. Holthoff, E. Sagnak, and O. Witte, Functional mapping of cortical areas with optical imaging, NeuroImage, vol.37, issue.2, pp.440-448, 2007.
DOI : 10.1016/j.neuroimage.2007.04.059

K. Miller, D. Pinto, and D. Simons, Processing in layer 4 of the neocortical circuit: new insights from visual and somatosensory cortex, Current Opinion in Neurobiology, vol.11, issue.4, pp.488-497, 2001.
DOI : 10.1016/S0959-4388(00)00239-7

W. Bosking, Y. Zhang, B. Schofield, and D. Fitzpatrick, Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex, The Journal of Neuroscience, vol.17, issue.6, p.2112, 1997.

C. Schreiner, Order and disorder in auditory cortical maps, Current Opinion in Neurobiology, vol.5, issue.4, pp.489-496, 1995.
DOI : 10.1016/0959-4388(95)80010-7

C. Wessinger, M. Buonocore, C. Kussmaul, and G. Mangun, Tonotopy in human auditory cortex examined with functional magnetic resonance imaging, Human Brain Mapping, vol.112, issue.1, pp.18-25, 1997.
DOI : 10.1002/(SICI)1097-0193(1997)5:1<18::AID-HBM3>3.0.CO;2-Q

P. König and N. Krüger, Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions, Biological Cybernetics, vol.4, issue.2, pp.325-334, 2006.
DOI : 10.1007/s00422-006-0050-3

M. Spratling, Predictive coding as a model of biased competition in visual attention, Vision Research, vol.48, issue.12, pp.1391-1408, 2008.
DOI : 10.1016/j.visres.2008.03.009

K. Friston, Learning and inference in the brain, Neural Networks, vol.16, issue.9, pp.1325-1352, 2003.
DOI : 10.1016/j.neunet.2003.06.005

R. P. Rao and D. H. Ballard, Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects, Nature Neuroscience, vol.2, issue.1, pp.79-87, 1999.
DOI : 10.1038/4580

T. Jantvik, L. Gustafsson, and A. P. Papli´nskipapli´nski, A Self-Organized Artificial Neural Network Architecture for Sensory Integration with Applications to Letter-Phoneme Integration, Neural Computation, vol.23, issue.8, pp.2101-2139, 2011.
DOI : 10.1097/AUD.0b013e31818005bd

M. Johnsson, C. Balkenius, and G. Hesslow, Associative Self-Organizing Map, International Joint Conference on Computational Intelligence (IJCCI. Citeseer, pp.363-370, 2009.
DOI : 10.5772/13168

M. Lefort, Y. Boniface, and B. Girau, SOMMA: Cortically inspired paradigms for multimodal processing, The 2013 International Joint Conference on Neural Networks (IJCNN), 2013.
DOI : 10.1109/IJCNN.2013.6706959

URL : https://hal.archives-ouvertes.fr/hal-00859986

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

M. Cottrell, J. Fort, and G. Pagès, Theoretical aspects of the SOM algorithm, Neurocomputing, vol.21, issue.1-3, pp.119-138, 1998.
DOI : 10.1016/S0925-2312(98)00034-4

URL : https://hal.archives-ouvertes.fr/hal-00141470

C. Bishop and N. Nasrabadi, Pattern recognition and machine learning, 2006.

R. B. Rusu, N. Blodow, Z. C. Marton, and M. Beetz, Aligning point cloud views using persistent feature histograms, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3384-3391, 2008.
DOI : 10.1109/IROS.2008.4650967

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.2661

L. Caron, Y. Song, D. Filliat, and A. Gepperth, Neural network based 2d/3d fusion for robotic object recognition, European Symposium on Articificial Neural Networks, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01012090

P. Oudeyer, Intelligent adaptive curiosity: a source of selfdevelopment, 2004.