Doiron Brent, Litwin-Kumar Ashok, Rosenbaum Robert, Ocker Gabriel K, Josić Krešimir
Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.
Nat Neurosci. 2016 Mar;19(3):383-93. doi: 10.1038/nn.4242.
Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.
对大量神经群体进行同步记录正变得越来越普遍。群体活动的一个重要特征是，从记录的神经元对输出的脉冲序列在试验间存在相关波动。与单个神经元的放电率类似，相关活动可受到多种因素的调节，从觉醒和注意力状态的变化到学习和任务参与度。然而，这些变化背后的生理机制尚未完全明确。我们回顾了近期的理论成果，这些成果确定了三种调节脉冲序列相关性的独立机制：输入相关性的变化、内部波动以及单个神经元的传递函数。我们首先在前馈通路中研究这些机制，然后展示相同的方法如何解释循环网络中相关性的调节。这种对群体活动调节的机制性约束在高维神经数据的统计分析中将具有重要意义。
