Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan.
Department of Molecular Physiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Nat Commun. 2020 Feb 13;11(1):872. doi: 10.1038/s41467-020-14645-x.
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.
自然场景稀疏地激活初级视觉皮层 (V1) 中的神经元。然而，稀疏激活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，这些都尚未揭示。使用双光子钙成像，我们从数百个 V1 神经元记录了对自然图像的视觉反应，并在麻醉和清醒的小鼠中从神经活动重建了图像。令人惊讶的是，单个自然图像可由数量非常少的高反应性神经元线性解码，而其余神经元甚至会降低解码效果。此外，无论试验间的反应变异性如何，这些神经元都能可靠地在整个试验中表示图像。基于我们的结果，多样化、部分重叠的感受野确保了稀疏和可靠的表示。我们认为，在相应的神经元模式随试验而变化的情况下，信息可以得到可靠的表示，而仅收集高反应性神经元的活动是下游神经元的最佳解码策略。
