



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 graphtheoretical 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. 

