Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain functions involves not only the computational model itself but also the conceptual definition of the brain functions, the analysis of correspondence between the model and the brain, and the generation of new biologically plausible insights and hypotheses. In this research, the multisensory processing is conceptually defined as the effect of multisensory convergence on the generation of multisensory neurons and their integrated response products, i.e., multisensory integration. Thus, the computational model is the implementation of the multisensory convergence and the simulation of the neural processing acting upon the convergence. Next, the most important step in the modeling is analysis of how well the model represents the target, i.e., brain function. It is also related to validation of the model. One of the intuitive and powerful ways of validating the model is to apply methods standard to neuroscience for analyzing the results obtained from the model. In addition, methods such as statistical and graph-theoretical analyses are used to confirm the similarity between the model and the brain. This research takes both approaches to provide analyses from many different perspectives. Finally, the model and its simulations provide insight into multisensory processing, generating plausible hypotheses, which will need to be confirmed by real experimentation
