This thesis focuses on unifying multiple modal data flows that may be provided by sensors of an agent. This unification, inspired by psychological experiments like the ventriloquist effect, is based on detecting correlations which are defined as temporally recurrent spatial patterns that appear in the input flows. Learning of the input flow correlations space consists on sampling this space and generalizing these learned samples. This thesis proposed some functional paradigms for multimodal data processing, leading to the connectionist, generic, modular and cortically inspired architecture SOMMA (Self-Organizing Maps for Multimodal Association). In this model, each modal stimulus is processed in a cortical map. Interconnection of these maps provides an unifying multimodal data processing. Sampling and generalization of correlations are based on the constrained self-organization of each map. The model is characterised by a gradual emergence of these functional properties: monomodal properties lead to the emergence of 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 provides plasticity and robustness properties to the data processing in SOMMA. Classical artificial intelligence models usually miss such properties.

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   |texte=   Spatial learning of multimodal correlations in a cortically inspired way
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