Objective. Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network’s local and global connectivity to these patterns and information processing remains largely unknown. Approach. Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. Main results. In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. Significance. Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.

客观的。许多神经系统表现出自发的、时空的神经活动模式，这对于信息处理至关重要。虽然这些级联模式可能源自神经元之间突触连接的底层网络，但网络的局部和全局连接对这些模式和信息处理的精确贡献仍然在很大程度上未知。方法。在这里，我们展示了网络结构如何使用线性系统理论、网络控制理论和信息论的数学工具，通过经验和模拟尖峰神经元中的网络动力学支持信息处理。主要结果。特别是，我们展示了活动及其包含的信息在真实和模拟网络中经历循环。意义。从广义上讲，我们的结果证明了级联神经网络如何有助于需要神经元模式持久激活的认知能力，例如工作记忆或注意力。