J. Ian, M. Goodfellow, X. Mirza, A. Da, Y. Courville et al., An empirical investigation of catastrophic forgeting in gradientbased neural networks, 2013.

K. Tanaka, Inferotemporal Cortex and Object Vision, Annual Review of Neuroscience, vol.19, issue.1, pp.109-139, 1996.
DOI : 10.1146/annurev.ne.19.030196.000545

A. David, . Leopold, V. Igor, . Bondar, A. Martin et al., Norm-based face encoding by single neurons in the monkey inferotemporal cortex, Nature, issue.7102, pp.442572-575, 2006.

A. David, M. Ross, . Deroche, J. Thomas, and . Palmeri, Not just the norm: Exemplar-based models also predict face aftereffects, Psychonomic bulletin & review, vol.21, issue.1, pp.47-70, 2014.

A. Cynthia, B. Erickson, R. Jagadeesh, and . Desimone, Clustering of perirhinal neurons with similar properties following visual experience in adult monkeys, Nature neuroscience, vol.3, issue.11, pp.1143-1148, 2000.

B. Daniel, . Polley, E. Elizabeth, . Steinberg, M. Michael et al., Perceptual learning directs auditory cortical map reorganization through top-down influences. The journal of neuroscience, pp.4970-4982, 2006.

M. Norman and . Weinberger, The nucleus basalis and memory codes: Auditory cortical plasticity and the induction of specific, associative behavioral memory, Acetylcholine: Cognitive and Brain Functions, pp.268-284, 2003.

E. Michael and . Hasselmo, The role of acetylcholine in learning and memory, Current opinion in neurobiology, vol.16, issue.6, pp.710-715, 2006.

T. Edmund, . Rolls, . Baylis, V. Hasselmo, and . Nalwa, The effect of learning on the face selective responses of neurons in the cortex in the superior temporal sulcus of the monkey, Experimental Brain Research, vol.76, issue.1, pp.153-164, 1989.

T. Heskes, Energy functions for self-organizing maps. Kohonen maps, pp.303-316, 1999.

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

O. Sigaud, C. Salaün, and V. Padois, On-line regression algorithms for learning mechanical models of robots: A survey, Robotics and Autonomous Systems, vol.59, issue.12, pp.1115-1129, 2011.
DOI : 10.1016/j.robot.2011.07.006

URL : https://hal.archives-ouvertes.fr/hal-00629133

S. Vijayakumar, A. D. , and S. Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol.11, issue.4, pp.2602-2634, 2005.
DOI : 10.1162/089976602753284491

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Intelligent Signal Processing, pp.306-351, 2001.
DOI : 10.1109/5.726791

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

A. Klöckner, N. Pinto, Y. Lee, B. Catanzaro, P. Ivanov et al., PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, Parallel Computing, vol.38, issue.3, pp.157-174, 2012.
DOI : 10.1016/j.parco.2011.09.001

S. Van-der-walt, S. C. Colbert, and G. Varoquaux, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, vol.13, issue.2, 2011.
DOI : 10.1109/MCSE.2011.37

URL : https://hal.archives-ouvertes.fr/inria-00564007

S. Vijayakumar, S. Klanke, and S. Schaal, A library for locally weighted projection regression, Journal of Machine Learning Research (JMLR), vol.9, pp.623-626, 2008.