Principal temporal extensions of SOM: Overview

The resolution of multy variable complex problems such as the multy speaker speech recognition and independently of context, requires the application of neural structures. One tool which proves to be powerful in the classification field, is the kohonen map called SOM. This map is characterized by the representation of static data. Thus, we ought to enrich the intelligibility and the performance of this model in order to reach what biology imposes by handling a kind of logical pile "memory" with the introduction of temporal context which realise feedbacks that integrate respectively the leaky integrators concept for the TKM. The recurrent leaky integrators idea for the recurrent SOM ‘RSOM’ in an improvement of the TKM. More recently, the principle of self refer for the recursive SOM. In other case, we are introducing the possibility to obtain hybridization with GA in an attempt to reach the natural evolution of the human thought as regards to recognition.