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Our daily perceptual experience is driven by different neural mechanisms thatyield multisensory interaction as the interplay between exogenous stimuli andendogenous expectations. While the interaction of multisensory cues accordingto their spatiotemporal properties and the formation of multisensoryfeature-based representations have been widely studied, the interaction ofspatial-associative neural representations has received considerably lessattention. In this paper, we propose a neural network architecture that modelsthe interaction of spatial-associative representations to perform causalinference of audiovisual stimuli. We investigate the spatial alignment ofexogenous audiovisual stimuli modulated by associative congruence. In thespatial layer, topographically arranged networks account for the interaction ofaudiovisual input in terms of population codes. In the associative layer,congruent audiovisual representations are obtained via the experience-drivendevelopment of feature-based associations. Levels of congruency are obtained asa by-product of the neurodynamics of self-organizing networks, where the amountof neural activation triggered by the input can be expressed via a nonlineardistance function. Our novel proposal is that activity-driven levels ofcongruency can be used as top-down modulatory projections to spatiallydistributed representations of sensory input, e.g. semantically relatedaudiovisual pairs will yield a higher level of integration than unrelatedpairs. Furthermore, levels of neural response in unimodal layers may be seen assensory reliability for the dynamic weighting of crossmodal cues. We describe aseries of planned experiments to validate our model in the tasks ofmultisensory interaction on the basis of semantic congruence and unimodal cuereliability.

Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception