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Conference Papers Year : 2013

SOMMA: Cortically Inspired Paradigms for Multimodal Processing

Mathieu Lefort
Yann Boniface
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Bernard Girau


SOMMA (Self Organizing Maps for Multimodal Association) consists on cortically inspired paradigms for multi-modal data processing. SOMMA defines generic cortical maps - one 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. 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 and of a modulation mechanism for influencing its self-organization 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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hal-00859986 , version 1 (09-09-2013)


  • HAL Id : hal-00859986 , version 1


Mathieu Lefort, Yann Boniface, Bernard Girau. SOMMA: Cortically Inspired Paradigms for Multimodal Processing. International Joint Conference on Neural Networks, Aug 2013, Dallas, United States. ⟨hal-00859986⟩
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