Robust representation of natural images by sparse and variable population of active neurons in visual cortex
Robust representation of natural images by sparse and variable population of active neurons in visual cortex
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DOI：
10.1101/300863
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发表时间：
2018-07
期刊：
影响因子：
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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质(V1)的神经元。然而，稀疏活跃的神经元是否以及如何充分和健壮地代表自然图像内容还没有被揭示。我们根据小鼠V1的神经元活动重建了自然图像。单个自然图像可以从数量惊人的少量(~20)高响应性神经元中线性解码。这是通过少量反应神经元的不同感受野(RF)实现的。此外，这些神经元强健地代表了试验到试验反应可变性的图像。具有部分重叠的RFs的同步神经元形成了功能簇，并在相同的试验中表现活跃。重要的是，多个集群代表了相似的局部图像模式，但在不同的试验中表现活跃。因此，各分组之间的活动的整合导致了针对变异性的强有力的表述。我们的结果表明，多样化的、部分重叠的RF确保了稀疏和健壮的表示，并提出了一种新的表示方案，其中信息被可靠地表示，同时表示神经元模式的变化。
Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, whether and how sparsely active neurons sufficiently and robustly represent natural image contents has not been revealed. We reconstructed the natural images from neuronal activities of mouse V1. Single natural images were linearly decodable from surprisingly small number (~20) of highly responsive neurons. This was achieved by diverse receptive fields (RFs) of the small number of responsive neurons. Furthermore, these neurons robustly represented the image against trial-to-trial response variability. Synchronous neurons with partially overlapping RFs formed functional clusters and were active at the same trials. Importantly, multiple clusters represented similar patterns of local images but were active at different trials. Thus, integration of activities among the clusters led to robust representation against the variability. Our results suggest that the diverse, partially overlapping RFs ensure the sparse and robust representation, and propose a new representation scheme in which information is reliably represented, while representing neuronal patterns change across trials.