This paper introduces CxSOM, a model to build modular architectures based on self-organizing maps (SOM). An original consensus driven approach enables to adress non-hierarchical architectures where SOMs get organized jointly. The paper aims at showing how the modules are able to store the association between data, and evaluating, by a mutual information criterion, the resulting organization. These results stand as preliminary work to study bigger architectures.

The motivation of our work is the instantiation of a computational view of the cerebral cortex. Kohonen's early definition of self-organizing maps was inspired by the cortical substrate on a local scale and is now a widely used learning algorithm. Following the same path, from biology to computation, the cortex can be interpreted as an architecture made of similar self-organizing modules connected together. To our knowledge, there are no such algorithmic derivation of large architectures of self-organizing modules. This paper presents the behavior of several maps connected one to another as a step towards wider networks of self-organizing maps and shows that this architecture learns a model of inputs and generates predictions in a map without using an additional algorithm. This prediction ability is applied to the control of a quadcopter flying in a corridor.