Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.
1] Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA. [2] Bernstein Center for Computational Neuroscience, Tübingen, Germany. [3] Werner-Reichardt-Center for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Germany.
Nat Neurosci. 2014 Jun;17(6):851-7. doi: 10.1038/nn.3707. Epub 2014 Apr 20.
Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.
神经代码被认为是适应自然环境的统计特性的。然而，在自然视觉输入期间，支配视觉皮层中集合活动组织的原则尚不清楚。我们在小鼠初级视觉皮层中记录了多达 500 个神经元的群体，并对其活动结构进行了描述，将对自然电影的反应与对照刺激的反应进行了比较。我们发现，自然场景中的高阶相关性导致了更稀疏的编码，其中信息通过一小部分神经元的可靠激活进行编码，并且可以更容易地读取。这种对自然场景有利的计算编码是状态依赖的，并且仅在麻醉和活跃的清醒动物中出现，而在安静的清醒状态下则不会出现。我们的结果表明，稀疏化具有功能优势，这可能是支配整个皮层微电路中群体活动结构的一般原则。
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