Computational and mathematical approaches to multisensory integration: theory and models
• 大类 : 医学 - 4区
• 小类 : 数学跨学科应用 - 3区
Every day, the human brain has to deal with a load of information carried by different sensory signals (visual, auditory, tactile, olfactory, vestibular) in different combinations, in time and space. We must employ specific “strategies” aimed at interpreting the stream of information and optimizing the probability of a successful response to external events. It has been revealed that one of the most important mechanisms the brain employs is multisensory integration (MSI). It is defined as the neural process by which unisensory signals are combined to form a new product that is significantly different from the responses evoked by the modality-specific component stimuli. Over the years, a number of rather ubiquitous empirical “rules” characterizing multisensory processing have been identified both in electrophysiological and behavioral data. Pivotal experiments by Barry Stein and colleagues helped to move away from a classic view of a brain with segregated sensory specific pathways to a more general idea of a “multisensory brain”, where regions previously categorized as “unisensory” now are known to exchange information, even at the earliest sensory processing stage in the cortex. Behavioral experiments further reinforced these findings. Numerous colleagues collected empirical evidence suggesting that MSI is a fundamental mechanism of the brain to interact with the external world, not limited to perceptual processes but involving higher order cognitive processes, such as decision making, semantics and language. These studies have produced a large body of knowledge about MSI, its fundamental characteristics, and the brain regions involved. More recently, several mathematical and computational approaches have been proposed to characterize MSI at different levels of description, but the goal of a unifying theoretical framework of MSI still seems out of reach. This Special Issue aims at presenting state-of-the-art theoretical and modeling efforts to better understand MSI. Papers involving computational and/or mathematical approaches with a clear reference to empirical data including any level of granularity, from single spike analysis to complex behavior, are most welcome.