Natural images are reliably represented by sparse and variable populations of neurons in visual cortex
Natural images are reliably represented by sparse and variable populations of neurons in visual cortex
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DOI：
10.1038/s41467-020-14645-x
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发表时间：
2020-02
影响因子：
16.6
通讯作者：
Takashi Yoshida;K. Ohki
中科院分区：
文献类型：
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作者：
Takashi Yoshida;K. Ohki
自然场景稀疏地激活初级视觉皮层（V1）的神经元。然而，稀疏活跃的神经元如何可靠地表示复杂的自然图像，以及如何从这些表示中最佳地解码信息还没有被揭示。使用双光子钙成像，我们记录了数百个V1神经元对自然图像的视觉反应，并从麻醉和清醒小鼠的神经活动中重建图像。单个自然图像可以从数量少得令人惊讶的高度响应的神经元中线性解码，剩余的神经元甚至会降低解码。此外，这些神经元可靠地表示跨试验的图像，而不管试验间的反应变化。根据我们的研究结果，多样的，部分重叠的感受野确保稀疏和可靠的表示。我们认为，信息是可靠的，而相应的神经元模式在试验中发生变化，只收集高反应神经元的活动是下游神经元的最佳解码策略。
Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons reliably represent complex natural images and how the information is optimally decoded from these representations have not been revealed. Using two-photon calcium imaging, we recorded visual responses to natural images from several hundred V1 neurons and reconstructed the images from neural activity in anesthetized and awake mice. A single natural image is linearly decodable from a surprisingly small number of highly responsive neurons, and the remaining neurons even degrade the decoding. Furthermore, these neurons reliably represent the image across trials, regardless of trial-to-trial response variability. Based on our results, diverse, partially overlapping receptive fields ensure sparse and reliable representation. We suggest that information is reliably represented while the corresponding neuronal patterns change across trials and collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons.