Apprentissage spatial de corrélations multimodales par des mécanismes d'inspiration corticale. (Spatial learning of multimodal correlations in a cortically inspired way)

This thesis focuses on unifying multiple modal data flows that may be provided by sensors of anagent. This unification, inspired by psychological experiments like the ventriloquist effect, is based ondetecting correlations which are defined as temporally recurrent spatial patterns that appear in the inputflows. Learning of the input flow correlations space consists on sampling this space and generalizing theselearned samples. This thesis proposed some functional paradigms for multimodal data processing, leadingto the connectionist, generic, modular and cortically inspired architecture SOMMA (Self-Organizing Mapsfor Multimodal Association). In this model, each modal stimulus is processed in a cortical map. Inter-connection of these maps provides an unifying multimodal data processing. Sampling and generalizationof correlations are based on the constrained self-organization of each map. The model is characterisedby a gradual emergence of these functional properties : monomodal properties lead to the emergenceof multimodal ones and learning of correlations in each map precedes self-organization of these maps.Furthermore, the use of a connectionist architecture and of on-line and unsupervised learning providesplasticity and robustness properties to the data processing in SOMMA. Classical artificial intelligencemodels usually miss such properties.