Ju Harang, Kim Jason Z, Beggs John M, Bassett Danielle S
Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
J Neural Eng. 2020 Nov 4;17(5):056045. doi: 10.1088/1741-2552/abbff1.
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.
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.
In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks.
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.
许多神经系统会显示出自发的神经活动时空模式，这些模式对信息处理至关重要。虽然这些级联模式可能源自神经元之间潜在的突触连接网络，但该网络的局部和全局连通性对这些模式及信息处理的精确贡献仍 largely 未知。
在此，我们运用线性系统理论、网络控制理论和信息理论中的数学工具，展示了在经验性和模拟的脉冲神经元中，网络结构如何通过网络动力学来支持信息处理。
特别地，我们表明活动及其所包含的信息在真实和模拟网络中通过循环传播。
总体而言，我们的结果证明了级联神经网络如何能够对需要神经元模式持续激活的认知能力做出贡献，例如工作记忆或注意力。
