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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Sparse low-order interaction network underlies a highly correlated and learnable neural population code
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Sparse low-order interaction network underlies a highly correlated and learnable neural population code

机译：稀疏的低阶交互网络是高度相关且可学习的神经种群代码的基础

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摘要

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 ~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.
机译：信息是通过大组神经元的联合活动模式在大脑中传递的。了解种群神经代码的结构和功能具有挑战性，因为可能的活动模式和神经元之间的依赖性呈指数级增长。我们在此报告，对于响应自然刺激的约100个视网膜神经元组，基于配对的模型（对于小型网络非常准确）已不再足够。我们表明，由于神经代码的稀疏性，可以使用一种新颖的模型轻松地学习高阶交互，并且一个非常稀疏的低阶交互网络是大量神经元代码的基础。此外，我们显示出交互网络是以分层和模块化的方式组织的，这暗示了可伸缩性。我们的结果表明，可学习性可能是神经代码的关键特征。

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