Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Hansastrasse 9a, 79104, Freiburg, Germany.
Nat Rev Neurosci. 2010 Sep;11(9):615-27. doi: 10.1038/nrn2886.
The brain is a highly modular structure. To exploit modularity, it is necessary that spiking activity can propagate from one module to another while preserving the information it carries. Therefore, reliable propagation is one of the key properties of a candidate neural code. Surprisingly, the conditions under which spiking activity can be propagated have received comparatively little attention in the experimental literature. By contrast, several computational studies in the last decade have addressed this issue. Using feedforward networks (FFNs) as a generic network model, they have identified two dynamical activity modes that support the propagation of either asynchronous (rate code) or synchronous (temporal code) spiking. Here, we review the dichotomy of asynchronous and synchronous propagation in FFNs, propose their integration into a single extended conceptual framework and suggest experimental strategies to test our hypothesis.
大脑是一个高度模块化的结构。为了利用模块化，脉冲活动必须能够从一个模块传播到另一个模块，同时保持其所携带的信息。因此，可靠的传播是候选神经编码的关键特性之一。令人惊讶的是，在实验文献中，脉冲活动可以传播的条件受到的关注相对较少。相比之下，过去十年中的几项计算研究已经解决了这个问题。他们使用前馈网络 (FFN) 作为通用网络模型，确定了两种动态活动模式，这两种模式支持异步（速率码）或同步（时间码）脉冲的传播。在这里，我们回顾了 FFN 中异步和同步传播的二分法，提出将它们整合到一个单一的扩展概念框架中，并提出了测试我们假设的实验策略。
