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Abstract

The brain receives information about the environment from all the sensory modalities, including vision, touch and audition. To efficiently interact with the environment, this information must eventually converge in the brain in order to form a reliable and accurate multimodal percept. This process is often complicated by the existence of noise at every level of signal processing, which makes the sensory information derived from the world imprecise and potentially inaccurate. There are several ways in which the nervous system may minimize the negative consequences of noise in terms of precision and accuracy. Two key strategies are to combine redundant sensory estimates and to utilize acquired knowledge about the statistical regularities of different sensory signals. In this lecture, I elaborate on how these strategies may be used by the nervous system in order to obtain the best possible estimates from noisy sensory signals, such that we are able of efficiently interact with the environment. Particularly, I will focus on the learning aspects and how our perceptions are tuned to the statistical regularities of an ever changing environment.