Barreiro Andrea K, Ly Cheng
Department of Mathematics, Southern Methodist University, Dallas, Texas, United States of America.
Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, United States of America.
PLoS Comput Biol. 2017 Apr 27;13(4):e1005506. doi: 10.1371/journal.pcbi.1005506. eCollection 2017 Apr.
A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate-a relationship previously explained in feedforward networks driven by correlated input-emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix.
神经科学中的一个核心问题是理解嘈杂的放电模式如何用于传递信息。由于神经放电是有噪声的，放电模式通常通过成对相关性或两个细胞在其基线放电率之上同时放电的概率来量化。实验中经常观察到的一个现象是，相关性会随着放电率系统地增加。理论研究已经确定，随放电率增加的刺激依赖性相关性对信息编码可能有有益影响；然而，我们对哪些电路机制会或不会产生这种相关性 - 放电率关系仍不完全理解。在这里，我们研究了具有基于电导的突触的循环耦合兴奋性 - 抑制性放电网络中，成对相关性与放电率之间的关系。我们发现，随着兴奋性耦合增强，成对相关性与放电率之间出现了正相关关系。为了解释这些发现，我们使用线性响应理论来预测完整的相关矩阵，并根据图基序分解相关性。然后，我们利用这种分解来解释为什么相关性与放电率的协变关系（这种关系先前在由相关输入驱动的前馈网络中得到了解释）在一些循环网络中出现，而在其他网络中不出现。此外，当相关性与放电率协变时，这种关系反映在相关矩阵的低秩结构中。
