神经系统对物理刺激的编码被认为取决于各种不同输入信号之间的关联性，但这个理论很少被经验验证。Jon Cafaro 和 Fred Rieke介绍了同时测量视网膜神经节细胞的激发态和抑制态电导性的新的记录方法，发现激发态和抑制态输入信号是强关联的，从而取消了彼此的输出。在将这些电导性变化重新引入有关联性或没有关联性的细胞时，他们发现，正如理论工作所预测的那样，关联性显著提高尖峰响应的可靠性。（生物谷Bioon.com）
生物谷推荐原文出处：
Nature doi:10.1038/nature09570
Noise correlations improve response fidelity and stimulus encoding
Jon Cafaro& Fred Rieke
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 computations1, 2, 3, 4, 5, 6, 7, 8. 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.
