Department of Neurobiology, The Weizmann Institute of Science, Rehovot 76100, Israel.
Proc Natl Acad Sci U S A. 2011 Jun 7;108(23):9679-84. doi: 10.1073/pnas.1019641108. Epub 2011 May 20.
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.
信息是由大量神经元的联合活动模式在大脑中传递的。由于可能的活动模式和神经元之间的依赖性呈指数级增长，因此理解群体神经编码的结构和功能具有挑战性。我们在这里报告，对于响应自然刺激的大约 100 个视网膜神经元的群体，基于成对的模型虽然对于小网络非常准确，但已经不再足够。我们表明，由于神经编码的稀疏性，更高阶的相互作用可以很容易地使用一种新模型来学习，并且一个非常稀疏的低阶相互作用网络是大群体神经元编码的基础。此外，我们表明，相互作用网络以分层和模块化的方式组织，这暗示了可扩展性。我们的结果表明，可学习性可能是神经编码的一个关键特征。
