Modeling Multisensory Enhancement with Self-organizing Maps

Abstract

Self-organization, a process by which the internal organization of a system changes without supervision, has been proposed as a possible basis for multisensory enhancement (MSE) in the superior colliculus (Anastasio and Patton, 2003). We simplify and extend these results by presenting a simulation using traditional self-organizing maps, intended to understand and simulate MSE as it may generally occur throughout the central nervous system. This simulation of MSE: (1) uses a standard unsupervised competitive learning algorithm, (2) learns from artificially generated activation levels corresponding to driven and spontaneous stimuli from separate and combined input channels, (3) uses a sigmoidal transfer function to generate quantifiable responses to separate inputs, (4) enhances the responses when those same inputs are combined, (5) obeys the inverse effectiveness principle of multisensory integration, and (6) can topographically congregate MSE in a manner similar to that seen in cortex. Thus, the model provides a useful method for evaluating and simulating the development of enhanced interactions between responses to different sensory modalities.