Associative Self-Organizing Map

We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative Self-Organizing Map (A-SOM). The A-SOM is similar to the SOM and thus develops a representation of its input space, but in addition it also learns to associate its activity with the activity of one or several external SOMs. The A-SOM has relevance in e.g. the modelling of expectations in one modality due to the activity invoked in another modality, and in the modelling of the neuroscientific simulation hypothesis. The paper presents the algorithm generalized to an arbitrary number of associated activities together with simulation results to find out about its performance and its ability to generalize to new inputs that it has not been trained on. The simulation results were very encouraging and confirmed the ability of the A-SOM to learn to associate the representations of its input space with the representations of the input spaces developed in two connected SOMs. Good generalization ability was also demonstrated