Topic: Bayesian brain hypothesis

An important open question in vision neuroscience is how the brain integrates prior knowledge with sensory data to perceive the world. The Bayesian brain hypothesis suggests that the brain performs posterior inference based on an internal model to construct representations that explain the stimulus. Indeed, there is support for this hypothesis in vision. Vision is not a passive process; rather, it actively recovers the features of an object from the signal cast on the retina. Even when faced with a cluttered scene of overlapping objects, the visual system quickly parses positional relationship

How the activity of sensory neurons supports perceptual decisions remains one of the central questions of systems neuroscience. Two primary frameworks guide the field’s thinking on this question. One framework, guided by ideas of information maximization, redundancy reduction, and efficient coding, focuses on the decision-related information represented by neural responses and how it is extracted from sensory populations. In another framework, based on the Bayesian brain hypothesis, sensory responses represent posterior beliefs about the outside world, which then guide decision-making. Interes