Coarse-to-fine processing drives the efficient coding of natural scenes in mouse visual cortex.

Coarse-to-fine processing drives the efficient coding of natural scenes in mouse visual cortex.
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由粗到精的处理驱动了小鼠视觉皮层对自然场景的有效编码。

DOI:
10.1016/j.celrep.2022.110606
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发表时间:
2022-03-29
期刊:
影响因子:
8.8
通讯作者:
Cang, Jianhua
Cang, Jianhua
中科院分区:
生物学1区
文献类型:
--
作者:
Skyberg, Rolf;Tanabe, Seiji;Chen, Hui;Cang, Jianhua

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视觉系统按顺序处理感官输入,先感知粗略信息,再感知细微细节。在这里,我们研究了从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质神经元以从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暂的反应期内迅速将其空间频率偏好从低到高改变所驱动的。这种转变改变了群体反应的方式,抵消了自然场景的统计规律,从而减少了冗余,并产生了更有效的神经表示。无论是黑暗饲养的小鼠还是麻醉的小鼠,表征效率的提高都没有发生,这表明从粗略到精细的空间加工显著减弱。总而言之,这些结果表明,从粗略到精细的加工是状态依赖的,通过出生后通过视觉经验发展,并通过生成更有效地表示与行为学相关的自然场景的复杂空间统计而提供计算优势。Skyberg等人。研究表明,在单个神经元的时间动力学的驱动下,老鼠的视觉皮质以从粗到精的方式处理自然场景。这些需要视觉经验来发展的反应动力学减少了神经编码中的冗余,并导致了对复杂视觉刺激的更有效的表示。
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. Skyberg et al. show that the visual cortex of mice processes natural scenes in a coarse-to-fine manner, driven by individual neuron’s temporal dynamics. These response dynamics, which require visual experience to develop, reduce redundancy in the neural code and lead to more efficient representations of complex visual stimuli.
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