@article {3871,
	title = {Invariant Recognition Shapes Neural Representations of Visual Input},
	journal = {Annual Review of Vision Science},
	volume = {4},
	year = {2018},
	month = {10/2018},
	pages = {403 - 422},
	abstract = {<p>Recognizing the people, objects, and actions in the world around us is a crucial aspect of human perception that allows us to plan and act in our environment. Remarkably, our proficiency in recognizing semantic categories from visual input is unhindered by transformations that substantially alter their appearance (e.g., changes in lighting or position). The ability to generalize across these complex transformations is a hallmark of human visual intelligence, which has been the focus of wide-ranging investigation in systems and computational neuroscience. However, while the neural machinery of human visual perception has been thoroughly described, the computational principles dictating its functioning remain unknown. Here, we review recent results in brain imaging, neurophysiology, and computational neuroscience in support of the hypothesis that the ability to support the invariant recognition of semantic entities in the visual world shapes which neural representations of sensory input are computed by human visual cortex.</p>
},
	keywords = {computational neuroscience, Invariance, neural decoding, visual representations},
	issn = {2374-4642},
	doi = {10.1146/annurev-vision-091517-034103},
	url = {https://www.annualreviews.org/doi/10.1146/annurev-vision-091517-034103},
	author = {Andrea Tacchetti and Leyla Isik and Tomaso Poggio}
}
