Bridging the gap between vision and language: a morphodynamical model of spatial categories

We propose a spiking neural network for mapping the infinity of schematic visual scenes to a small set of semantic symbols. According to cognitive linguistics, spatial prepositions such as 'in' or 'above' are neutral toward the shape and size of objects. We suggest that they correspond to morphodynamical transforms erasing details and creating virtual structures (boundaries, skeleton). These singularities arise from a large-scale lattice of coupled excitable units exhibiting pattern formation through spatiotemporal order, especially traveling waves. Our model addresses the fundamental cognitive mechanisms of spatial schematization and categorization, which mediate vision and language and are crucial in designing intelligent systems.