Cooperation in Self-Organizing Map Networks Enhances Information Transmission from Input to Output in the Presence of Input Background Activity

The self-organizing map (SOM) algorithm produces artificial neural maps by simulating competition and cooperation among neurons. We study the consequences of input background activity on simulated self-organization, using the SOM, of the retinotopic map in the superior colliculus. The colliculus not only represents its inputs but also uses them to localize saccadic targets. Using the colliculus as a test-bed enables us to quantify the results of self-organization both descriptively, in terms of input-output mutual information, and functionally, in terms of the probability of error (expected distortion) in localizing targets. We find that mutual information is low, and distortion is high, when the SOM operates in the presence of input background activity but without the cooperative component (no neighbor training). Cooperation (training neighbors) greatly increases mutual information and greatly decreases distortion. Neighbor training can produce maps, but the simulations suggest that cooperative mechanisms are needed to produce information gain in the presence of input background activity. Map formation may be merely a side effect.