Department of Biology and Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA.
Department of Biology and Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA.
Cell Rep. 2022 Mar 29;38(13):110606. doi: 10.1016/j.celrep.2022.110606.
The visual system processes sensory inputs sequentially, perceiving coarse information before fine details. Here we study the neural basis of coarse-to-fine processing and its computational benefits in natural vision. We find that primary visual cortical neurons in awake mice respond to natural scenes in a coarse-to-fine manner, primarily driven by individual neurons rapidly shifting their spatial frequency preference from low to high over a brief response period. This shift transforms the population response in a way that counteracts the statistical regularities of natural scenes, thereby reducing redundancy and generating a more efficient neural representation. The increase in representational efficiency does not occur in either dark-reared or anesthetized mice, which show significantly attenuated coarse-to-fine spatial processing. Collectively, these results illustrate that coarse-to-fine processing is state dependent, develops postnatally via visual experience, and provides a computational advantage by generating more efficient representations of the complex spatial statistics of ethologically relevant natural scenes.
视觉系统顺序地处理感觉输入，在感知精细细节之前感知粗糙信息。在这里，我们研究了粗到精处理的神经基础及其在自然视觉中的计算优势。我们发现，清醒小鼠的初级视觉皮层神经元以粗到精的方式对自然场景做出反应，主要是由单个神经元在短时间的反应期内迅速将其空间频率偏好从低切换到高驱动的。这种转变以一种抵消自然场景统计规律的方式转换群体反应，从而减少冗余并产生更有效的神经表示。这种表示效率的提高不会出现在暗适应或麻醉的小鼠中，这些小鼠显示出明显减弱的粗到精的空间处理。总的来说，这些结果表明，粗到精的处理是状态依赖的，通过视觉经验在出生后发展，并通过生成更有效的与行为相关的自然场景的复杂空间统计表示来提供计算优势。
