Given the diversity of data, it is essential to provide artificial systems with a capacity of association of information. The aim of the work we present in this paper is to provide a new neuromimetic system with a capacity of association of correlated information coming from different external inputs. This system has a multimodal architecture, consisting of CNTF cards constituted of spiking neurons with inter-cards and intra-cards connections. We present a study of the functional properties of this system and the conditions of convergence to a stable state preparing the system to emerge this association and to learn it. Then, we present a model of the STDP learning rule that we adapted to the dynamics of our system. To evaluate the learning ability of our system, we developed a scenario of memorization and reminder that we explain in this article.
