Barreiro Andrea K, Ly Cheng
Department of Mathematics, Southern Methodist University, Dallas, USA.
Department of Statistical Science and Operations Research, Virginia Commonwealth University, Richmond, USA.
J Math Neurosci. 2018 Jun 6;8(1):8. doi: 10.1186/s13408-018-0063-y.
The structure of spiking activity in cortical networks has important implications for how the brain ultimately codes sensory signals. However, our understanding of how network and intrinsic cellular mechanisms affect spiking is still incomplete. In particular, whether cell pairs in a neural network show a positive (or no) relationship between pairwise spike count correlation and average firing rate is generally unknown. This relationship is important because it has been observed experimentally in some sensory systems, and it can enhance information in a common population code. Here we extend our prior work in developing mathematical tools to succinctly characterize the correlation and firing rate relationship in heterogeneous coupled networks. We find that very modest changes in how heterogeneous networks occupy parameter space can dramatically alter the correlation-firing rate relationship.
皮层网络中尖峰活动的结构对于大脑最终如何编码感觉信号具有重要意义。然而，我们对于网络和内在细胞机制如何影响尖峰的理解仍然不完整。特别是，神经网络中的细胞对在成对尖峰计数相关性和平均发放率之间是否呈现正相关（或无相关）关系通常尚不清楚。这种关系很重要，因为它已在一些感觉系统中通过实验观察到，并且它可以增强共同群体编码中的信息。在此，我们扩展了我们之前的工作，即开发数学工具以简洁地描述异构耦合网络中的相关性和发放率关系。我们发现，异构网络占据参数空间方式的非常微小的变化都可能极大地改变相关性 - 发放率关系。
