Editing Computational neuroscience (section)

===Visual attention, identification, and categorization===
Visual attention can be described as a set of mechanisms that limit some processing to a subset of incoming stimuli.<ref>{{cite book|author1=Marvin M. Chun |author2=Jeremy M. Wolfe |author3=E. B. Goldstein | title=Blackwell Handbook of Sensation and Perception |url=https://archive.org/details/blackwellhandboo00gold |url-access=limited | publisher=Blackwell Publishing Ltd |year=2001 | pages=[https://archive.org/details/blackwellhandboo00gold/page/n284 272]–310 |isbn=978-0-631-20684-2}}</ref> Attentional mechanisms shape what we see and what we can act upon. They allow for concurrent selection of some (preferably, relevant) information and inhibition of other information. In order to have a more concrete specification of the mechanism underlying visual attention and the binding of features, a number of computational models have been proposed aiming to explain psychophysical findings. In general, all models postulate the existence of a saliency or priority map for registering the potentially interesting areas of the retinal input, and a gating mechanism for reducing the amount of incoming visual information, so that the limited computational resources of the brain can handle it.<ref>{{cite book|author1=Edmund Rolls |author2=Gustavo Deco | title=Computational Neuroscience of Vision | publisher=Oxford Scholarship Online | year=2012 | isbn=978-0-198-52488-5}}</ref>
An example theory that is being extensively tested behaviorally and physiologically is the  [[V1 Saliency Hypothesis]]  that a bottom-up saliency map is created in the primary visual cortex to guide attention exogenously.<ref name=Li2002 /> Computational neuroscience provides a mathematical framework for studying the mechanisms involved in brain function and allows complete simulation and prediction of neuropsychological syndromes.