6We present in this paper a variation of the self-organising map algorithm where the original

SOMMA (Self Organizing Maps for Multimodal Association) consists on cortically inspired paradigms for multimodal data processing. SOMMA defines generic cortical mapsone for each modality -composed of 3-layers cortical columns. Each column learns a discrimination to a stimulus of the input flow with the BCMu learning rule [26]. These discriminations are self-organized in each map thanks to the coupling with neural fields used as a neighborhood function [25]. Learning and computation in each map is influenced by other modalities thanks to bidirectional topographic connections between all maps. This multimodal influence drives a joint self-organization of maps and multimodal perceptions of stimuli. This work takes place after the design of a self-organizing map [25] and of a modulation mechanism for influencing its self-organization [26] oriented towards a multimodal purpose. In this paper, we introduce a way to connect these self-organizing maps to obtain a multimap multimodal processing, completing our previous work. We also give an overview of the architectural and functional properties of the resulting paradigm SOMMA.

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Human beings interact with the environment through different modalities, i.e. perceptions and actions, processed in the cortex by dedicated brain areas. These areas are self-organized, so that spatially close neurons are sensitive to close stimuli, providing generalization from previously learned examples. Although perceptive flows are picked up by different spatially separated sensors, their processings are not isolated. On the contrary, they are constantly interacting, as illustrated by the McGurk effect. When the auditory stimulus /ba/ and the /ga/ lip movement are presented simultaneously, people perceive a /da/, which does not correspond to any of the stimuli. Merging several stimuli into one multimodal perception reduces ambiguities and noises and is essential to interact with the environment. This article proposes a model for modality association, inspired by the biological properties of the cortex. The model consists of modality maps interacting through an associative map to raise a consistent multimodal perception of the environment. We propose the coupling of BCM learning rule and neural maps to obtain the decentralized and unsupervised self-organization of each modal map influenced by the multisensory context. We obtain local self-organization of modal maps with various inputs and discretizations.

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