期刊
FRONTIERS IN CELLULAR NEUROSCIENCE
卷 16, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncel.2022.1006703
关键词
natural scene analysis; visual cortex (VC); textures analysis; efficient coding hypothesis; sensory system
资金
- NSF [PHY-1734030, CISE 2212519]
- NIH [R01EB026945]
This article reviews recent findings that demonstrate the adaptation of central circuits in the visual brain to key aspects of natural scenes. It also discusses the potential role of adapting to natural temporal statistics in learning and representing visual objects, and proposes two challenges for future research.
Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This efficient coding principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.
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