L. Alvarez and J. Morel, Formalization and computational aspects of image analysis, Acta Numerica, vol.1, pp.1-59, 1994.
DOI : 10.1137/0729052

G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, Applied Mathematical Sciences, vol.147, 2002.

G. Bugmann, Biologically plausible neural computation, Biosystems, vol.40, issue.1-2, pp.11-19, 1997.
DOI : 10.1016/0303-2647(96)01625-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.9106

J. Bullier, R. Deriche, O. Faugeras, D. Fieze, P. Girard et al., Rivage feedback during visual integration : towards a generic architecture, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00070556

G. Cottet and M. E. Ayyadi, A Volterra type model for image processing, IEEE Transactions on Image Processing, vol.7, issue.3, 1998.
DOI : 10.1109/83.661179

R. Courant, Calculus of Variations, 1946.

P. Degond and S. Mas-gallic, The weighted particle method for convection-diffusion equations, Mathematics of Computation, vol.53, issue.188, pp.485-525, 1989.

R. Deriche, O. Faugeras, G. Giraudon, T. Papadopoulo, and R. Vaillant, Four Applications of Differential Geometry to Computer Vision, Artificial and Biological Vision Systems, Basic Research Series, pp.93-141, 1993.
DOI : 10.1007/978-3-642-77840-7_5

L. Evans, Partial Differential Equations, Graduate Studies in Mathematics. Proceedings, vol.19, 1998.

O. Faugeras, Three-Dimensional Computer Vision: a Geometric Viewpoint, 1993.

W. Gerstner and W. M. Kistler, Mathematical formulations of Hebbian learning, Biological Cybernetics, vol.87, issue.5-6, pp.404-415, 2002.
DOI : 10.1007/s00422-002-0353-y

G. Hermosillo, Variational Methods for Multimodal Image Matching, 2002.
URL : https://hal.archives-ouvertes.fr/tel-00457459

G. Hermosillo and C. , Chefd'hotel, and O. Faugeras. Variational methods for multimodal image matching, International Journal of Computer Vision, vol.50, issue.3, pp.329-343, 2002.
DOI : 10.1023/A:1020830525823

T. Huang and A. Netravali, Motion and structure from feature correspondences: A review, Proc. IEEE, pp.252-268, 1994.

P. Kornprobst, R. Peeters, T. Viville, G. Malandain, S. Mierisova et al., Superresolution in MRI and its influence in statistical analysis, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00072075

A. Leonard, Vortex methods for flow simulation, Journal of Computational Physics, vol.37, issue.3, pp.289-335, 1980.
DOI : 10.1016/0021-9991(80)90040-6

H. Nagel and W. Enkelmann, An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.5, pp.565-593, 1986.
DOI : 10.1109/TPAMI.1986.4767833

M. Otte and H. Nagel, Optical flow estimation: Advances and comparisons, Proceedings of the 3rd European Conference on Computer Vision, pp.51-70, 1994.
DOI : 10.1007/3-540-57956-7_5

R. Rao and T. J. Sejnowski, Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning, Neural Computation, vol.3, issue.1, pp.2221-2237, 2001.
DOI : 10.1038/25665

P. Raviat, An analysis of particle methods, Numerical Methods in Fluid Dynamics Lecture Notes in Math, vol.1127, pp.243-324, 1985.
DOI : 10.1007/BFb0074532

A. Tikhonov, Regularization of incorrectly posed problems, Soviet. Math. Dokl, vol.4, pp.1624-1627, 1963.

D. Tschumperlé, PDE's Based Regularization of Multivalued Images and Applications, 2002.

D. Tschumperlé and R. Deriche, Vector-valued image regularization with PDE's: a common framework for different applications, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003.
DOI : 10.1109/CVPR.2003.1211415

D. Tschumperl and R. Deriche, Constrained and unconstrained PDE's for vector image restoration, Proceedings of the 10th Scandinavian Conference on Image Analysis, pp.153-160, 2001.

T. Viéville, Biologically plausible regularization mechanisms, 8th ICCNS, 2004.

T. Viville, Biologically plausible regularization mechanisms, 2002.

T. Viville, Towards biologically plausible regularization mechanisms, 2003.

T. Viville and O. D. Faugeras, Robust and fast computation of unbiased intensity derivatives in images, Proceedings of the 2nd ECCV, pp.203-211, 1992.
DOI : 10.1007/3-540-55426-2_22

T. Viville, D. Lingrand, and F. Gaspard, Implementing a multi-model estimation method, The International Journal of Computer Vision, vol.44, issue.1, 2001.

A. J. Yu, M. Giese, and T. Poggio, Biophysiologically Plausible Implementations of the Maximum Operation, Neural Computation, vol.12, issue.4, 2003.
DOI : 10.1162/neco.1989.1.3.334