Abstract

In this contribution, an information theoretic approach to human perception is proposed. Initially, a model is derived to characterize the stimulus , as perceived by the brain, in response to an event . Then, an optimal estimation algorithm is conceived to infer the information  brought by , knowing the stochastic properties of . In this way, it becomes possible to mimic the short-term brain response to an exogenous stimulus. In addition, a Bayesian framework is supposed to learn the parameters that characterize  from past experiences, thus emulating the long-term response of the brain. Finally, a generalization of the proposed approach is discussed to demonstrate its flexibility and adaptability to different contexts.