Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA.
Nature. 2010 Dec 16;468(7326):964-7. doi: 10.1038/nature09570. Epub 2010 Dec 5.
Computation in the nervous system often relies on the integration of signals from parallel circuits with different functional properties. Correlated noise in these inputs can, in principle, have diverse and dramatic effects on the reliability of the resulting computations. Such theoretical predictions have rarely been tested experimentally because of a scarcity of preparations that permit measurement of both the covariation of a neuron's input signals and the effect on a cell's output of manipulating such covariation. Here we introduce a method to measure covariation of the excitatory and inhibitory inputs a cell receives. This method revealed strong correlated noise in the inputs to two types of retinal ganglion cell. Eliminating correlated noise without changing other input properties substantially decreased the accuracy with which a cell's spike outputs encoded light inputs. Thus, covariation of excitatory and inhibitory inputs can be a critical determinant of the reliability of neural coding and computation.
神经系统中的计算通常依赖于具有不同功能特性的并行电路信号的整合。这些输入中的相关噪声原则上可以对计算结果的可靠性产生多样化和戏剧性的影响。由于缺乏允许同时测量神经元输入信号的协变和操纵这种协变对细胞输出影响的制剂，因此这些理论预测很少得到实验验证。在这里，我们引入了一种测量细胞接收的兴奋性和抑制性输入的协变的方法。该方法揭示了两种类型的视网膜神经节细胞的输入中存在强烈的相关噪声。在不显著改变其他输入特性的情况下消除相关噪声会大大降低细胞的尖峰输出编码光输入的准确性。因此，兴奋性和抑制性输入的协变可能是神经编码和计算可靠性的关键决定因素。
