神經(jīng)系統(tǒng)對物理刺激的編碼被認(rèn)為取決于各種不同輸入信號之間的關(guān)聯(lián)性,，但這個理論很少被經(jīng)驗驗證,。Jon Cafaro 和 Fred Rieke介紹了同時測量視網(wǎng)膜神經(jīng)節(jié)細(xì)胞的激發(fā)態(tài)和抑制態(tài)電導(dǎo)性的新的記錄方法，發(fā)現(xiàn)激發(fā)態(tài)和抑制態(tài)輸入信號是強關(guān)聯(lián)的,，從而取消了彼此的輸出,。在將這些電導(dǎo)性變化重新引入有關(guān)聯(lián)性或沒有關(guān)聯(lián)性的細(xì)胞時，他們發(fā)現(xiàn),，正如理論工作所預(yù)測的那樣,，關(guān)聯(lián)性顯著提高尖峰響應(yīng)的可靠性。（生物谷Bioon.com）
生物谷推薦原文出處：
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.
