Correlation between neural spike trains increases with firing rate
Correlation between neural spike trains increases with firing rate
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DOI：
10.1038/nature06028
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发表时间：
2007-08-16
期刊：
影响因子：
64.8
通讯作者：
Reyes, Alex
中科院分区：
文献类型：
--
作者：
de la Rocha, Jaime;Doiron, Brent;Reyes, Alex
视网膜（1-3）、嗅觉系统（4）、视觉（5）和躯体感觉（6）、丘脑和几个皮质区域（7-10）中的神经元群体显示出它们的动作电位（尖峰序列）的放电时间之间的时间相关性。相关放电与刺激编码（9），注意力（11），刺激识别（4）和运动行为（12）有关。然而，相关尖峰的机制知之甚少（2，3，13 -20），其编码含义仍有争议（13，16，21，22）。例如，两个神经元放电之间的相关性是否仅仅由它们的传入电流之间的相关性决定，或者它们是否也取决于输入的均值和方差，这一点还不清楚。我们解决了这个问题，通过计算在体外皮层神经元接收相关输入的未连接对的尖峰序列相关系数。值得注意的是，即使当输入的相关性保持固定，尖峰序列输出的相关性增加与放电率，但在很大程度上是独立的尖峰序列的变异性。结合分析技术和数值模拟使用'集成和消防'神经元模型，我们表明，这种输出相关性和放电率之间的关系是强大的输入异质性。最后，用一个标准的门限线性模型来验证这个被忽略的关系，证明了结果的普适性。尖峰活动的速率和相关性之间的这种联系将神经代码的两个基本特征联系起来。
Populations of neurons in the retina(1-3), olfactory system(4), visual(5) and somatosensory(6) thalamus, and several cortical regions(7-10) show temporal correlation between the discharge times of their action potentials ( spike trains). Correlated firing has been linked to stimulus encoding(9), attention(11), stimulus discrimination(4), and motor behaviour(12). Nevertheless, the mechanisms underlying correlated spiking are poorly understood(2,3,13-20), and its coding implications are still debated(13,16,21,22). It is not clear, for instance, whether correlations between the discharges of two neurons are determined solely by the correlation between their afferent currents, or whether they also depend on the mean and variance of the input. We addressed this question by computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs. Notably, even when the input correlation remained fixed, the spike train output correlation increased with the firing rate, but was largely independent of spike train variability. With a combination of analytical techniques and numerical simulations using 'integrate- and- fire' neuron models we show that this relationship between output correlation and firing rate is robust to input heterogeneities. Finally, this overlooked relationship is replicated by a standard threshold- linear model, demonstrating the universality of the result. This connection between the rate and correlation of spiking activity links two fundamental features of the neural code.