Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Nat Neurosci. 2020 Aug;23(8):908-917. doi: 10.1038/s41593-020-0653-3. Epub 2020 Jun 15.
A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.
一组神经元可以产生活动模式，这些模式代表关于刺激的信息；随后，该组可以通过突触将活动模式转换和传输到空间分布的区域。最近的神经科学研究已经开始分别研究信息处理的两个组成部分：神经活动中的刺激表示和模拟神经相互作用的网络中的信息传输。然而，最近才开始研究将这两种方法联系起来。在这里，我们简要回顾了这两个独立的文献领域；然后，我们回顾了为解决这一差距而取得的最新进展。我们继续讨论活动模式如何从一种表示形式演变为另一种表示形式，形成在基础网络上展开的动态表示。我们的目标是提供一个整体框架来理解和描述神经信息表示和传输，同时揭示未来研究的令人兴奋的前沿。
