Efficient coding of natural images in the mouse visual cortex
Efficient coding of natural images in the mouse visual cortex
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DOI：
10.1038/s41467-024-45919-3
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发表时间：
2024-03-19
影响因子：
16.6
通讯作者：
Benucci，Andrea
中科院分区：
文献类型：
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作者：
Bolanos，Federico;Orlandi，Javier G.;Benucci，Andrea
神经元的活动是如何产生自然视觉的，这仍然是一个需要深入研究的问题。中层视觉区沿着腹侧流是选择性的一个共同的类的自然图像纹理，但这种选择性的电路水平的理解和它的联系感知仍然不清楚。我们在小鼠中解决了这些问题，首先表明它们可以在感知上区分纹理和统计上更简单的光谱匹配刺激，以及纹理类型。然后，在神经水平上，我们发现，次级视觉区（LM）表现出更高程度的选择性纹理相比，初级视觉区（V1）。此外，纹理在不同的神经活动子空间中表示，其相对距离与图像的统计相似性和小鼠区分它们的能力相关。值得注意的是，这些依赖性在LM中更明显，其中纹理相关的子空间小于V1，从而导致上级刺激解码能力。总之，我们的研究结果表明纹理视觉小鼠，刺激统计，神经表征和感知灵敏度之间的联系框架，一个独特的标志，有效的编码计算。
How the activity of neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream are selective to a common class of natural images—textures—but a circuit-level understanding of this selectivity and its link to perception remains unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between textures and statistically simpler spectrally matched stimuli, and between texture types. Then, at the neural level, we found that the secondary visual area (LM) exhibited a higher degree of selectivity for textures compared to the primary visual area (V1). Furthermore, textures were represented in distinct neural activity subspaces whose relative distances were found to correlate with the statistical similarity of the images and the mice’s ability to discriminate between them. Notably, these dependencies were more pronounced in LM, where the texture-related subspaces were smaller than in V1, resulting in superior stimulus decoding capabilities. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity—a distinct hallmark of efficient coding computations.