Sparse low-order interaction network underlies a highly correlated and learnable neural population code
Sparse low-order interaction network underlies a highly correlated and learnable neural population code
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DOI：
10.1073/pnas.1019641108
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发表时间：
2011-06-07
影响因子：
11.1
通讯作者：
Schneidman, Elad
中科院分区：
文献类型：
--
作者：
Ganmor, Elad;Segev, Ronen;Schneidman, Elad
信息在大脑中是由大量神经元的联合活动模式所承载的。由于可能的活动模式数量呈指数级增长以及神经元之间存在相互依存关系，理解群体神经编码的结构和功能具有挑战性。我们在此报告，对于约100个对自然刺激作出反应的视网膜神经元群体而言，基于两两关系的模型（对于小型网络非常准确）已不再适用。我们表明，由于神经编码的稀疏性，可以使用一种新模型轻松了解高阶相互作用，并且一个非常稀疏的低阶相互作用网络是大量神经元编码的基础。此外，我们还表明，相互作用网络是以分层和模块化的方式组织的，这暗示了其可扩展性。我们的研究结果表明，可学习性可能是神经编码的一个关键特征。
Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of similar to 100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code.